497 lines
53 KiB
BibTeX
497 lines
53 KiB
BibTeX
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@inreference{2021naivebayesclassifier,
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title = {Naive {{Bayes}} Classifier},
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booktitle = {Wikipedia},
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date = {2021-05-21T00:32:52Z},
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url = {https://en.wikipedia.org/w/index.php?title=Naive_Bayes_classifier&oldid=1024247473},
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urldate = {2021-06-03},
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abstract = {In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (na\"ive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve higher accuracy levels.Na\"ive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers. In the statistics and computer science literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but na\"ive Bayes is not (necessarily) a Bayesian method.},
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annotation = {Page Version ID: 1024247473},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/5T4T73X4/index.html},
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langid = {english}
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}
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@inproceedings{alshangiti2019whydevelopingmachine,
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title = {Why Is {{Developing Machine Learning Applications Challenging}}? {{A Study}} on {{Stack Overflow Posts}}},
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shorttitle = {Why Is {{Developing Machine Learning Applications Challenging}}?},
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booktitle = {2019 {{ACM}}/{{IEEE International Symposium}} on {{Empirical Software Engineering}} and {{Measurement}} ({{ESEM}})},
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author = {Alshangiti, Moayad and Sapkota, Hitesh and Murukannaiah, Pradeep K. and Liu, Xumin and Yu, Qi},
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date = {2019-09},
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pages = {1--11},
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publisher = {{IEEE}},
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location = {{Porto de Galinhas, Recife, Brazil}},
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doi = {10.1109/ESEM.2019.8870187},
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abstract = {Method: We conduct an empirical study of ML-related developer posts on Stack Overflow. We perform in-depth quantitative and qualitative analyses focusing on a series of research questions related to the challenges of developing ML applications and the directions to address them. Results: Our findings include: (1) ML questions suffer from a much higher percentage of unanswered questions on Stack Overflow than other domains; (2) there is a lack of ML experts in the Stack Overflow QA community; (3) the data preprocessing and model deployment phases are where most of the challenges lay; and (4) addressing most of these challenges require more ML implementation knowledge than ML conceptual knowledge. Conclusions: Our findings suggest that most challenges are under the data preparation and model deployment phases, i.e., early and late stages. Also, the implementation aspect of ML shows much higher difficulty level among developers than the conceptual aspect.},
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eventtitle = {2019 {{ACM}}/{{IEEE International Symposium}} on {{Empirical Software Engineering}} and {{Measurement}} ({{ESEM}})},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/IEEE/Alshangiti_2019_Why is Developing Machine Learning Applications Challenging.pdf},
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isbn = {978-1-72812-968-6},
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langid = {english}
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}
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@inproceedings{amershi-2019-softwareengineeringmachine,
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title = {Software {{Engineering}} for {{Machine Learning}}: {{A Case Study}}},
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shorttitle = {Software {{Engineering}} for {{Machine Learning}}},
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booktitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{Software Engineering}} in {{Practice}} ({{ICSE}}-{{SEIP}})},
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author = {Amershi, Saleema and Begel, Andrew and Bird, Christian and DeLine, Robert and Gall, Harald and Kamar, Ece and Nagappan, Nachiappan and Nushi, Besmira and Zimmermann, Thomas},
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date = {2019-05},
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pages = {291--300},
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publisher = {{IEEE}},
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location = {{Montreal, QC, Canada}},
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doi = {10.1109/ICSE-SEIP.2019.00042},
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abstract = {Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components \textemdash{} models may be ``entangled'' in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.},
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eventtitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{Software Engineering}} in {{Practice}} ({{ICSE}}-{{SEIP}})},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/IEEE/Amershi_2019_Software Engineering for Machine Learning.pdf},
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isbn = {978-1-72811-760-7},
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langid = {english}
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}
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@inproceedings{bangash2019whatdevelopersknow,
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title = {What Do {{Developers Know About Machine Learning}}: {{A Study}} of {{ML Discussions}} on {{StackOverflow}}},
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shorttitle = {What Do {{Developers Know About Machine Learning}}},
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booktitle = {2019 {{IEEE}}/{{ACM}} 16th {{International Conference}} on {{Mining Software Repositories}} ({{MSR}})},
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author = {Bangash, Abdul Ali and Sahar, Hareem and Chowdhury, Shaiful and Wong, Alexander William and Hindle, Abram and Ali, Karim},
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date = {2019-05},
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pages = {260--264},
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publisher = {{IEEE}},
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location = {{Montreal, QC, Canada}},
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doi = {10.1109/MSR.2019.00052},
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abstract = {Machine learning, a branch of Artificial Intelligence, is now popular in software engineering community and is successfully used for problems like bug prediction, and software development effort estimation. Developers' understanding of machine learning, however, is not clear, and we require investigation to understand what educators should focus on, and how different online programming discussion communities can be more helpful. We conduct a study on Stack Overflow (SO) machine learning related posts using the SOTorrent dataset. We found that some machine learning topics are significantly more discussed than others, and others need more attention. We also found that topic generation with Latent Dirichlet Allocation (LDA) can suggest more appropriate tags that can make a machine learning post more visible and thus can help in receiving immediate feedback from sites like SO.},
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eventtitle = {2019 {{IEEE}}/{{ACM}} 16th {{International Conference}} on {{Mining Software Repositories}} ({{MSR}})},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/IEEE/Bangash_2019_What do Developers Know About Machine Learning.pdf},
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isbn = {978-1-72813-412-3},
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langid = {english}
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}
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@article{borges2016understandingfactorsthat,
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title = {Understanding the {{Factors}} That {{Impact}} the {{Popularity}} of {{GitHub Repositories}}},
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author = {Borges, Hudson and Hora, Andre and Valente, Marco Tulio},
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date = {2016-10},
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journaltitle = {2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
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pages = {334--344},
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doi = {10.1109/ICSME.2016.31},
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abstract = {Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which could help them on building and evolving systems in a competitive software market.},
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archiveprefix = {arXiv},
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eprint = {1606.04984},
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eprinttype = {arxiv},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Borges_2016_Understanding the Factors that Impact the Popularity of GitHub Repositories.pdf},
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keywords = {Computer Science - Social and Information Networks,Computer Science - Software Engineering},
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langid = {english}
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}
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@online{bujokas2020textclassificationusing,
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title = {Text Classification Using Word Embeddings and Deep Learning in Python \textemdash{} Classifying Tweets From\ldots},
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author = {Bujokas, Eligijus},
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date = {2020-03-16T04:26:03},
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url = {https://medium.com/analytics-vidhya/text-classification-using-word-embeddings-and-deep-learning-in-python-classifying-tweets-from-6fe644fcfc81},
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urldate = {2021-05-21},
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abstract = {The purpose of this article is to help a reader understand how to leverage word embeddings and deep learning when creating a text\ldots},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/BDS956UP/text-classification-using-word-embeddings-and-deep-learning-in-python-classifying-tweets-from-6.html},
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langid = {english},
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organization = {{Medium}}
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}
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@inproceedings{chaparro2017detectingmissinginformation,
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title = {Detecting Missing Information in Bug Descriptions},
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booktitle = {Proceedings of the 2017 11th {{Joint Meeting}} on {{Foundations}} of {{Software Engineering}}},
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author = {Chaparro, Oscar and Lu, Jing and Zampetti, Fiorella and Moreno, Laura and Di Penta, Massimiliano and Marcus, Andrian and Bavota, Gabriele and Ng, Vincent},
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date = {2017-08-21},
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pages = {396--407},
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publisher = {{ACM}},
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location = {{Paderborn Germany}},
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doi = {10.1145/3106237.3106285},
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abstract = {Bug reports document unexpected software behaviors experienced by users. To be effective, they should allow bug triagers to easily understand and reproduce the potential reported bugs, by clearly describing the Observed Behavior (OB), the Steps to Reproduce (S2R), and the Expected Behavior (EB). Unfortunately, while considered extremely useful, reporters often miss such pieces of information in bug reports and, to date, there is no effective way to automatically check and enforce their presence. We manually analyzed nearly 3k bug reports to understand to what extent OB, EB, and S2R are reported in bug reports and what discourse patterns reporters use to describe such information. We found that (i) while most reports contain OB (i.e., 93.5\%), only 35.2\% and 51.4\% explicitly describe EB and S2R, respectively; and (ii) reporters recurrently use 154 discourse patterns to describe such content. Based on these findings, we designed and evaluated an automated approach to detect the absence (or presence) of EB and S2R in bug descriptions. With its best setting, our approach is able to detect missing EB (S2R) with 85.9\% (69.2\%) average precision and 93.2\% (83\%) average recall. Our approach intends to improve bug descriptions quality by alerting reporters about missing EB and S2R at reporting time.},
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eventtitle = {{{ESEC}}/{{FSE}}'17: {{Joint Meeting}} of the {{European Software Engineering Conference}} and the {{ACM SIGSOFT Symposium}} on the {{Foundations}} of {{Software Engineering}}},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/ACM/Chaparro_2017_Detecting missing information in bug descriptions.pdf},
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isbn = {978-1-4503-5105-8},
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langid = {english}
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}
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@inproceedings{chen2015deepdrivinglearningaffordance,
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title = {{{DeepDriving}}: {{Learning Affordance}} for {{Direct Perception}} in {{Autonomous Driving}}},
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shorttitle = {{{DeepDriving}}},
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author = {Chen, Chenyi and Seff, Ari and Kornhauser, Alain and Xiao, Jianxiong},
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date = {2015},
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pages = {2722--2730},
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url = {https://openaccess.thecvf.com/content_iccv_2015/html/Chen_DeepDriving_Learning_Affordance_ICCV_2015_paper.html},
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urldate = {2021-06-09},
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eventtitle = {Proceedings of the {{IEEE International Conference}} on {{Computer Vision}}},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/4ZE2KTJC/Chen_DeepDriving_Learning_Affordance_ICCV_2015_paper.html}
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}
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@article{deboom2016representationlearningvery,
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title = {Representation Learning for Very Short Texts Using Weighted Word Embedding Aggregation},
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author = {De Boom, Cedric and Van Canneyt, Steven and Demeester, Thomas and Dhoedt, Bart},
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date = {2016-09},
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journaltitle = {Pattern Recognition Letters},
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shortjournal = {Pattern Recognition Letters},
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volume = {80},
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pages = {150--156},
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issn = {01678655},
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doi = {10.1016/j.patrec.2016.06.012},
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abstract = {Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.},
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archiveprefix = {arXiv},
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eprint = {1607.00570},
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eprinttype = {arxiv},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/De Boom_2016_Representation learning for very short texts using weighted word embedding.pdf},
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keywords = {Computer Science - Computation and Language,Computer Science - Information Retrieval},
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langid = {english}
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}
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@inproceedings{duenas2018percevalsoftwareproject,
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title = {Perceval: Software Project Data at Your Will},
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shorttitle = {Perceval},
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booktitle = {Proceedings of the 40th {{International Conference}} on {{Software Engineering}}: {{Companion Proceeedings}}},
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author = {Due\~nas, Santiago and Cosentino, Valerio and Robles, Gregorio and Gonzalez-Barahona, Jesus M.},
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date = {2018-05-27},
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pages = {1--4},
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publisher = {{Association for Computing Machinery}},
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location = {{New York, NY, USA}},
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doi = {10.1145/3183440.3183475},
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abstract = {Software development projects, in particular open source ones, heavily rely on the use of tools to support, coordinate and promote development activities. Despite their paramount value, they contribute to fragment the project data, thus challenging practitioners and researchers willing to derive insightful analytics about software projects. In this demo we present Perceval, a loyal helper able to perform automatic and incremental data gathering from almost any tool related with contributing to open source development, among others, source code management, issue tracking systems, mailing lists, forums, and social media. Perceval is an industry strong free software tool that has been widely used in Bitergia, a company devoted to offer commercial software analytics of software projects. It hides the technical complexities related to data acquisition and eases the definition of analytics. A video showcasing the main features of Perceval can be found at https://youtu.be/eH1sYF0Hdc8.},
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isbn = {978-1-4503-5663-3},
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keywords = {empirical software engineering,open source software,software analytics,software development,software mining},
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series = {{{ICSE}} '18}
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}
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@article{fan2021whatmakespopular,
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title = {What Makes a Popular Academic {{AI}} Repository?},
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author = {Fan, Yuanrui and Xia, Xin and Lo, David and Hassan, Ahmed E. and Li, Shanping},
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date = {2021-01},
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journaltitle = {Empirical Software Engineering},
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shortjournal = {Empir Software Eng},
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volume = {26},
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pages = {2},
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issn = {1382-3256, 1573-7616},
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doi = {10.1007/s10664-020-09916-6},
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abstract = {Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20\% repositories that have the most number of stars as popular, and we label the bottom 70\% repositories as unpopular. The remaining 10\% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features\textemdash indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community.},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Fan_2021_What makes a popular academic AI repository.pdf},
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langid = {english},
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number = {1}
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}
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@book{geron2019handsonmachinelearning,
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title = {Hands-{{On Machine Learning}} with {{Scikit}}-{{Learn}}, {{Keras}}, and {{TensorFlow}}: {{Concepts}}, {{Tools}}, and {{Techniques}} to {{Build Intelligent Systems}}},
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shorttitle = {Hands-{{On Machine Learning}} with {{Scikit}}-{{Learn}}, {{Keras}}, and {{TensorFlow}}},
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author = {G\'eron, Aur\'elien},
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date = {2019-09-05},
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publisher = {{"O'Reilly Media, Inc."}},
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abstract = {Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks\textemdash Scikit-Learn and TensorFlow\textemdash author Aur\'elien G\'eron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/O'Reilly Media, Inc./Geron_2019_Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.pdf},
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isbn = {978-1-4920-3259-5},
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keywords = {Computers / Computer Vision & Pattern Recognition,Computers / Data Processing,Computers / Intelligence (AI) & Semantics,Computers / Natural Language Processing,Computers / Neural Networks,Computers / Programming Languages / Python},
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langid = {english},
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pagetotal = {921}
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}
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@inproceedings{gonzalez2020statemluniverse10,
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title = {The {{State}} of the {{ML}}-Universe: 10 {{Years}} of {{Artificial Intelligence}} \& {{Machine Learning Software Development}} on {{GitHub}}},
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shorttitle = {The {{State}} of the {{ML}}-Universe},
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booktitle = {Proceedings of the 17th {{International Conference}} on {{Mining Software Repositories}}},
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author = {Gonzalez, Danielle and Zimmermann, Thomas and Nagappan, Nachiappan},
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date = {2020-06-29},
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pages = {431--442},
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publisher = {{ACM}},
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location = {{Seoul Republic of Korea}},
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doi = {10.1145/3379597.3387473},
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abstract = {In the last few years, artificial intelligence (AI) and machine learning (ML) have become ubiquitous terms. These powerful techniques have escaped obscurity in academic communities with the recent onslaught of AI \& ML tools, frameworks, and libraries that make these techniques accessible to a wider audience of developers. As a result, applying AI \& ML to solve existing and emergent problems is an increasingly popular practice. However, little is known about this domain from the software engineering perspective. Many AI \& ML tools and applications are open source, hosted on platforms such as GitHub that provide rich tools for large-scale distributed software development. Despite widespread use and popularity, these repositories have never been examined as a community to identify unique properties, development patterns, and trends.},
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eventtitle = {{{MSR}} '20: 17th {{International Conference}} on {{Mining Software Repositories}}},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/ACM/Gonzalez_2020_The State of the ML-universe.pdf},
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isbn = {978-1-4503-7517-7},
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langid = {english}
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}
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@inproceedings{grichi2020impactmultilanguagedevelopment,
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title = {On the {{Impact}} of {{Multi}}-Language {{Development}} in {{Machine Learning Frameworks}}},
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booktitle = {2020 {{IEEE International Conference}} on {{Software Maintenance}} and {{Evolution}} ({{ICSME}})},
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author = {Grichi, Manel and Eghan, Ellis E. and Adams, Bram},
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date = {2020-09},
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pages = {546--556},
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publisher = {{IEEE}},
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location = {{Adelaide, Australia}},
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doi = {10.1109/ICSME46990.2020.00058},
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abstract = {The role of machine learning frameworks in soft\- ware applications has exploded in recent years. Similar to non-machine learning frameworks, those frameworks need to evolve to incorporate new features, optimizations, etc., yet their evolution is impacted by the interdisciplinary development teams needed to develop them: scientists and developers. One concrete way in which this shows is through the use of multiple pro\- gramming languages in their code base, enabling the scientists to write optimized low-level code while developers can integrate the latter into a robust framework. Since multi-language code bases have been shown to impact the development process, this paper empirically compares ten large open-source multi-language machine learning frameworks and ten large open-source multi\- language traditional systems in terms of the volume of pull requests, their acceptance ratio i.e., the percentage of accepted pull requests among all the received pull requests, review process duration i.e., period taken to accept or reject a pull request, and bug-proneness. We find that multi-language pull request contributions present a challenge for both machine learning and traditional systems. Our main findings show that in both machine learning and traditional systems, multi-language pull requests are likely to be less accepted than mono-language pull requests; it also takes longer for both multi- and mono-language pull requests to be rejected than accepted. Machine learning frameworks take longer to accept/reject a multi-language pull request than traditional systems. Finally, we find that mono\- language pull requests in machine learning frameworks are more bug-prone than traditional systems.},
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eventtitle = {2020 {{IEEE International Conference}} on {{Software Maintenance}} and {{Evolution}} ({{ICSME}})},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/IEEE/Grichi_2020_On the Impact of Multi-language Development in Machine Learning Frameworks.pdf},
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isbn = {978-1-72815-619-4},
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langid = {english}
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}
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@inproceedings{han2020empiricalstudydependency,
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title = {An {{Empirical Study}} of the {{Dependency Networks}} of {{Deep Learning Libraries}}},
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booktitle = {2020 {{IEEE International Conference}} on {{Software Maintenance}} and {{Evolution}} ({{ICSME}})},
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author = {Han, Junxiao and Deng, Shuiguang and Lo, David and Zhi, Chen and Yin, Jianwei and Xia, Xin},
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date = {2020-09},
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pages = {868--878},
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publisher = {{IEEE}},
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location = {{Adelaide, Australia}},
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doi = {10.1109/ICSME46990.2020.00116},
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abstract = {Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory study on the dependency networks of deep learning libraries, namely, Tensorflow, PyTorch, and Theano. We study the project purposes, application domains, dependency degrees, update behaviors and reasons as well as version distributions of deep learning projects that depend on Tensorflow, PyTorch, and Theano. Our study unveils some commonalities in various aspects (e.g., purposes, application domains, dependency degrees) of deep learning libraries and reveals some discrepancies as for the update behaviors, update reasons, and the version distributions. Our findings highlight some directions for researchers and also provide suggestions for deep learning developers and users.},
|
|
eventtitle = {2020 {{IEEE International Conference}} on {{Software Maintenance}} and {{Evolution}} ({{ICSME}})},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/IEEE/Han_2020_An Empirical Study of the Dependency Networks of Deep Learning Libraries2.pdf},
|
|
isbn = {978-1-72815-619-4},
|
|
langid = {english}
|
|
}
|
|
|
|
@article{han2020whatprogrammersdiscuss,
|
|
title = {What Do {{Programmers Discuss}} about {{Deep Learning Frameworks}}},
|
|
author = {Han, Junxiao and Shihab, Emad and Wan, Zhiyuan and Deng, Shuiguang and Xia, Xin},
|
|
date = {2020-07},
|
|
journaltitle = {Empirical Software Engineering},
|
|
shortjournal = {Empir Software Eng},
|
|
volume = {25},
|
|
pages = {2694--2747},
|
|
issn = {1382-3256, 1573-7616},
|
|
doi = {10.1007/s10664-020-09819-6},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Han_2020_What do Programmers Discuss about Deep Learning Frameworks.pdf},
|
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langid = {english},
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number = {4}
|
|
}
|
|
|
|
@online{hannun2014deepspeechscaling,
|
|
title = {Deep {{Speech}}: {{Scaling}} up End-to-End Speech Recognition},
|
|
shorttitle = {Deep {{Speech}}},
|
|
author = {Hannun, Awni and Case, Carl and Casper, Jared and Catanzaro, Bryan and Diamos, Greg and Elsen, Erich and Prenger, Ryan and Satheesh, Sanjeev and Sengupta, Shubho and Coates, Adam and Ng, Andrew Y.},
|
|
date = {2014-12-19},
|
|
url = {http://arxiv.org/abs/1412.5567},
|
|
urldate = {2021-06-09},
|
|
abstract = {We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0\% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.},
|
|
archiveprefix = {arXiv},
|
|
eprint = {1412.5567},
|
|
eprinttype = {arxiv},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Hannun_2014_Deep Speech.pdf;/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/JH3YQS9Z/1412.html},
|
|
keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing},
|
|
primaryclass = {cs}
|
|
}
|
|
|
|
@book{harrington2012machinelearningaction,
|
|
title = {Machine {{Learning}} in {{Action}}},
|
|
author = {Harrington, Peter},
|
|
date = {2012-04-19},
|
|
publisher = {{Manning Publications}},
|
|
abstract = {SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.About the BookA machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsideA no-nonsense introductionExamples showing common ML tasksEveryday data analysisImplementing classic algorithms like Apriori and AdaboosTable of ContentsPART 1 CLASSIFICATIONMachine learning basicsClassifying with k-Nearest NeighborsSplitting datasets one feature at a time: decision treesClassifying with probability theory: na\"ive BayesLogistic regressionSupport vector machinesImproving classification with the AdaBoost meta algorithmPART 2 FORECASTING NUMERIC VALUES WITH REGRESSIONPredicting numeric values: regressionTree-based regressionPART 3 UNSUPERVISED LEARNINGGrouping unlabeled items using k-means clusteringAssociation analysis with the Apriori algorithmEfficiently finding frequent itemsets with FP-growthPART 4 ADDITIONAL TOOLSUsing principal component analysis to simplify dataSimplifying data with the singular value decompositionBig data and MapReduce},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/Manning Publications/Harrington_2012_Machine Learning in Action.pdf},
|
|
isbn = {978-1-61729-018-3},
|
|
keywords = {Computers / Computer Science,Computers / Data Processing,Computers / Databases / Data Mining,Computers / Intelligence (AI) & Semantics,Computers / Mathematical & Statistical Software,Computers / Programming / Algorithms,Computers / Programming / Open Source,Computers / Programming Languages / Python},
|
|
langid = {english},
|
|
pagetotal = {384}
|
|
}
|
|
|
|
@inproceedings{hassan2009predictingfaultsusing,
|
|
title = {Predicting Faults Using the Complexity of Code Changes},
|
|
booktitle = {2009 {{IEEE}} 31st {{International Conference}} on {{Software Engineering}}},
|
|
author = {Hassan, Ahmed E.},
|
|
date = {2009},
|
|
pages = {78--88},
|
|
publisher = {{IEEE}},
|
|
location = {{Vancouver, BC, Canada}},
|
|
doi = {10.1109/ICSE.2009.5070510},
|
|
abstract = {Predicting the incidence of faults in code has been commonly associated with measuring complexity. In this paper, we propose complexity metrics that are based on the code change process instead of on the code. We conjecture that a complex code change process negatively affects its product, i.e., the software system. We validate our hypothesis empirically through a case study using data derived from the change history for six large open source projects. Our case study shows that our change complexity metrics are better predictors of fault potential in comparison to other well-known historical predictors of faults, i.e., prior modifications and prior faults.},
|
|
eventtitle = {2009 {{IEEE}} 31st {{International Conference}} on {{Software Engineering}}},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/IEEE/Hassan_2009_Predicting faults using the complexity of code changes.pdf},
|
|
isbn = {978-1-4244-3453-4},
|
|
langid = {english}
|
|
}
|
|
|
|
@inproceedings{he2016deepresiduallearning,
|
|
title = {Deep {{Residual Learning}} for {{Image Recognition}}},
|
|
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
|
|
date = {2016},
|
|
pages = {770--778},
|
|
url = {https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html},
|
|
urldate = {2021-06-09},
|
|
eventtitle = {Proceedings of the {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/B6WAY3GX/He_Deep_Residual_Learning_CVPR_2016_paper.html}
|
|
}
|
|
|
|
@article{hirschberg2015advancesnaturallanguage,
|
|
title = {Advances in Natural Language Processing},
|
|
author = {Hirschberg, Julia and Manning, Christopher D.},
|
|
date = {2015-07-17},
|
|
journaltitle = {Science},
|
|
volume = {349},
|
|
pages = {261--266},
|
|
publisher = {{American Association for the Advancement of Science}},
|
|
issn = {0036-8075, 1095-9203},
|
|
doi = {10.1126/science.aaa8685},
|
|
abstract = {Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area.},
|
|
eprint = {26185244},
|
|
eprinttype = {pmid},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/Z36CCHGE/261.html},
|
|
langid = {english},
|
|
number = {6245}
|
|
}
|
|
|
|
@online{humbatova-2019-taxonomyrealfaults,
|
|
title = {Taxonomy of {{Real Faults}} in {{Deep Learning Systems}}},
|
|
author = {Humbatova, Nargiz and Jahangirova, Gunel and Bavota, Gabriele and Riccio, Vincenzo and Stocco, Andrea and Tonella, Paolo},
|
|
date = {2019-11-07},
|
|
url = {http://arxiv.org/abs/1910.11015},
|
|
urldate = {2021-03-17},
|
|
abstract = {The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduce a large taxonomy of faults in deep learning (DL) systems. We have manually analysed 1059 artefacts gathered from GitHub commits and issues of projects that use the most popular DL frameworks (TensorFlow, Keras and PyTorch) and from related Stack Over ow posts. Structured interviews with 20 researchers and practitioners describing the problems they have encountered in their experience have enriched our taxonomy with a variety of additional faults that did not emerge from the other two sources. Our nal taxonomy was validated with a survey involving an additional set of 21 developers, con rming that almost all fault categories (13/15) were experienced by at least 50\% of the survey participants.},
|
|
archiveprefix = {arXiv},
|
|
eprint = {1910.11015},
|
|
eprinttype = {arxiv},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Humbatova_2019_Taxonomy of Real Faults in Deep Learning Systems.pdf},
|
|
keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Software Engineering},
|
|
langid = {english},
|
|
primaryclass = {cs}
|
|
}
|
|
|
|
@online{huval2015empiricalevaluationdeep,
|
|
title = {An {{Empirical Evaluation}} of {{Deep Learning}} on {{Highway Driving}}},
|
|
author = {Huval, Brody and Wang, Tao and Tandon, Sameep and Kiske, Jeff and Song, Will and Pazhayampallil, Joel and Andriluka, Mykhaylo and Rajpurkar, Pranav and Migimatsu, Toki and Cheng-Yue, Royce and Mujica, Fernando and Coates, Adam and Ng, Andrew Y.},
|
|
date = {2015-04-16},
|
|
url = {http://arxiv.org/abs/1504.01716},
|
|
urldate = {2021-06-09},
|
|
abstract = {Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.},
|
|
archiveprefix = {arXiv},
|
|
eprint = {1504.01716},
|
|
eprinttype = {arxiv},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/F6WDYMDF/1504.html},
|
|
keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Robotics},
|
|
primaryclass = {cs}
|
|
}
|
|
|
|
@article{liu2020deeplearningsystem,
|
|
title = {A Deep Learning System for Differential Diagnosis of Skin Diseases},
|
|
author = {Liu, Yuan and Jain, Ayush and Eng, Clara and Way, David H. and Lee, Kang and Bui, Peggy and Kanada, Kimberly and de Oliveira Marinho, Guilherme and Gallegos, Jessica and Gabriele, Sara and Gupta, Vishakha and Singh, Nalini and Natarajan, Vivek and Hofmann-Wellenhof, Rainer and Corrado, Greg S. and Peng, Lily H. and Webster, Dale R. and Ai, Dennis and Huang, Susan J. and Liu, Yun and Dunn, R. Carter and Coz, David},
|
|
date = {2020-06},
|
|
journaltitle = {Nature Medicine},
|
|
shortjournal = {Nat Med},
|
|
volume = {26},
|
|
pages = {900--908},
|
|
publisher = {{Nature Publishing Group}},
|
|
issn = {1546-170X},
|
|
doi = {10.1038/s41591-020-0842-3},
|
|
abstract = {Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80\% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/CGRLE4YH/s41591 020 0842 3.html},
|
|
issue = {6},
|
|
langid = {english},
|
|
number = {6},
|
|
options = {useprefix=true}
|
|
}
|
|
|
|
@article{liu2021exploratorystudyintroduction,
|
|
title = {An {{Exploratory Study}} on the {{Introduction}} and {{Removal}} of {{Different Types}} of {{Technical Debt}}},
|
|
author = {Liu, Jiakun and Huang, Qiao and Xia, Xin and Shihab, Emad and Lo, David and Li, Shanping},
|
|
date = {2021-03},
|
|
journaltitle = {Empirical Software Engineering},
|
|
shortjournal = {Empir Software Eng},
|
|
volume = {26},
|
|
pages = {16},
|
|
issn = {1382-3256, 1573-7616},
|
|
doi = {10.1007/s10664-020-09917-5},
|
|
abstract = {To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice.},
|
|
archiveprefix = {arXiv},
|
|
eprint = {2101.03730},
|
|
eprinttype = {arxiv},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Liu_2021_An Exploratory Study on the Introduction and Removal of Different Types of.pdf},
|
|
keywords = {Computer Science - Software Engineering},
|
|
langid = {english},
|
|
number = {2}
|
|
}
|
|
|
|
@online{multicolumndeepneural,
|
|
title = {Multi-Column Deep Neural Networks for Image Classification},
|
|
url = {https://ieeexplore.ieee.org/abstract/document/6248110/},
|
|
urldate = {2021-06-09},
|
|
abstract = {Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/2R4ZFR6C/6248110.html},
|
|
langid = {american}
|
|
}
|
|
|
|
@online{naturallanguagetoolkit,
|
|
title = {Natural {{Language Toolkit}} \textemdash{} {{NLTK}} 3.5 Documentation},
|
|
url = {https://www.nltk.org/},
|
|
urldate = {2021-03-30},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/VKI2452L/www.nltk.org.html}
|
|
}
|
|
|
|
@online{navlanilatentsemanticindexing,
|
|
title = {Latent {{Semantic Indexing}} Using {{Scikit}}-{{Learn}}},
|
|
author = {Navlani, Avinash},
|
|
url = {https://machinelearninggeek.com/latent-semantic-indexing-using-scikit-learn/},
|
|
urldate = {2021-05-17},
|
|
abstract = {In this tutorial, we will focus on Latent Semantic Indexing or Latent Semantic Analysis and perform topic modeling using Scikit-learn.},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/MB9PJVXP/latent-semantic-indexing-using-scikit-learn.html},
|
|
langid = {american}
|
|
}
|
|
|
|
@online{oktay2018attentionunetlearning,
|
|
title = {Attention {{U}}-{{Net}}: {{Learning Where}} to {{Look}} for the {{Pancreas}}},
|
|
shorttitle = {Attention {{U}}-{{Net}}},
|
|
author = {Oktay, Ozan and Schlemper, Jo and Folgoc, Loic Le and Lee, Matthew and Heinrich, Mattias and Misawa, Kazunari and Mori, Kensaku and McDonagh, Steven and Hammerla, Nils Y. and Kainz, Bernhard and Glocker, Ben and Rueckert, Daniel},
|
|
date = {2018-05-20},
|
|
url = {http://arxiv.org/abs/1804.03999},
|
|
urldate = {2021-06-09},
|
|
abstract = {We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.},
|
|
archiveprefix = {arXiv},
|
|
eprint = {1804.03999},
|
|
eprinttype = {arxiv},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/LBH6UYV8/1804.html},
|
|
keywords = {Computer Science - Computer Vision and Pattern Recognition},
|
|
primaryclass = {cs}
|
|
}
|
|
|
|
@article{rochkind1975sourcecodecontrol,
|
|
title = {The Source Code Control System},
|
|
author = {Rochkind, Marc J.},
|
|
date = {1975-12},
|
|
journaltitle = {IEEE Transactions on Software Engineering},
|
|
volume = {SE-1},
|
|
pages = {364--370},
|
|
issn = {1939-3520},
|
|
doi = {10.1109/TSE.1975.6312866},
|
|
abstract = {The Source Code Control System (SCCS) is a software tool designed to help programming projects control changes to source code. It provides facilities for storing, updating, and retrieving all versions of modules, for controlling updating privileges for identifying load modules by version number, and for recording who made each software change, when and where it was made, and why. This paper discusses the SCCS approach to source code control, shows how it is used and explains how it is implemented.},
|
|
eventtitle = {{{IEEE Transactions}} on {{Software Engineering}}},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/8KN2BXLY/6312866.html},
|
|
keywords = {Configuration management,Control systems,Documentation,Laboratories,Libraries,Process control,program maintenance,Software,software control,software project management},
|
|
number = {4}
|
|
}
|
|
|
|
@article{scalabrino2019listeningcrowdrelease,
|
|
title = {Listening to the {{Crowd}} for the {{Release Planning}} of {{Mobile Apps}}},
|
|
author = {Scalabrino, Simone and Russo, Barbara and Oliveto, Rocco},
|
|
date = {2019},
|
|
journaltitle = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING},
|
|
volume = {45},
|
|
pages = {19},
|
|
abstract = {The market for mobile apps is getting bigger and bigger, and it is expected to be worth over 100 Billion dollars in 2020. To have a chance to succeed in such a competitive environment, developers need to build and maintain high-quality apps, continuously astonishing their users with the coolest new features. Mobile app marketplaces allow users to release reviews. Despite reviews are aimed at recommending apps among users, they also contain precious information for developers, reporting bugs and suggesting new features. To exploit such a source of information, developers are supposed to manually read user reviews, something not doable when hundreds of them are collected per day. To help developers dealing with such a task, we developed CLAP (Crowd Listener for releAse Planning), a web application able to (i) categorize user reviews based on the information they carry out, (ii) cluster together related reviews, and (iii) prioritize the clusters of reviews to be implemented when planning the subsequent app release. We evaluated all the steps behind CLAP, showing its high accuracy in categorizing and clustering reviews and the meaningfulness of the recommended prioritizations. Also, given the availability of CLAP as a working tool, we assessed its applicability in industrial environments.},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/undefined/Scalabrino_2019_Listening to the Crowd for the Release Planning of Mobile Apps.pdf},
|
|
langid = {english},
|
|
number = {1}
|
|
}
|
|
|
|
@article{shannon1948mathematicaltheorycommunication,
|
|
title = {A Mathematical Theory of Communication},
|
|
author = {Shannon, C. E.},
|
|
date = {1948-07},
|
|
journaltitle = {The Bell System Technical Journal},
|
|
volume = {27},
|
|
pages = {379--423},
|
|
issn = {0005-8580},
|
|
doi = {10.1002/j.1538-7305.1948.tb01338.x},
|
|
abstract = {The recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A basis for such a theory is contained in the important papers of Nyquist1 and Hartley2 on this subject. In the present paper we will extend the theory to include a number of new factors, in particular the effect of noise in the channel, and the savings possible due to the statistical structure of the original message and due to the nature of the final destination of the information.},
|
|
eventtitle = {The {{Bell System Technical Journal}}},
|
|
file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/ZLGCL7V5/6773024.html},
|
|
number = {3}
|
|
}
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@inproceedings{zhang2018empiricalstudytensorflow,
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title = {An Empirical Study on {{TensorFlow}} Program Bugs},
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booktitle = {Proceedings of the 27th {{ACM SIGSOFT International Symposium}} on {{Software Testing}} and {{Analysis}}},
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author = {Zhang, Yuhao and Chen, Yifan and Cheung, Shing-Chi and Xiong, Yingfei and Zhang, Lu},
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date = {2018-07-12},
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pages = {129--140},
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publisher = {{ACM}},
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location = {{Amsterdam Netherlands}},
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doi = {10.1145/3213846.3213866},
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abstract = {Deep learning applications become increasingly popular in important domains such as self-driving systems and facial identity systems. Defective deep learning applications may lead to catastrophic consequences. Although recent research e orts were made on testing and debugging deep learning applications, the characteristics of deep learning defects have never been studied. To ll this gap, we studied deep learning applications built on top of TensorFlow and collected program bugs related to TensorFlow from StackOverow QA pages and Github projects. We extracted information from QA pages, commit messages, pull request messages, and issue discussions to examine the root causes and symptoms of these bugs. We also studied the strategies deployed by TensorFlow users for bug detection and localization. These ndings help researchers and TensorFlow users to gain a better understanding of coding defects in TensorFlow programs and point out a new direction for future research.},
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eventtitle = {{{ISSTA}} '18: {{International Symposium}} on {{Software Testing}} and {{Analysis}}},
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file = {/home/norangebit/Documenti/10-personal/12-organizzation/07-zotero-attachments/ACM/Zhang_2018_An empirical study on TensorFlow program bugs.pdf},
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isbn = {978-1-4503-5699-2},
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langid = {english}
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}
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