Notebooks provide an interactive environment for programmers to develop code, analyse data and inject interleaved visualisations in a single environment. Despite their flexibility, a major pitfall that data scientists encounter is unexpected behaviour caused by the unique out-of-order execution model of notebooks. As a result, data scientists face various challenges ranging from notebook correctness, reproducibility and cleaning. In this paper, we propose a framework that performs static analysis on notebooks, incorporating their unique execution semantics. Our framework is general in the sense that it accommodates a wide range of analyses, useful for various notebook use cases. We have instantiated our framework on a diverse set of analyses and have evaluated them on 2211 real world notebooks. Our evaluation demonstrates that the vast majority (98.7%) of notebooks can be analysed in less than a second, well within the time frame required by interactive notebook clients.
{"title":"A Static Analysis Framework for Data Science Notebooks","authors":"Pavle Suboti'c, Lazar Miliki'c, M. Stojic","doi":"10.1145/3510457.3513032","DOIUrl":"https://doi.org/10.1145/3510457.3513032","url":null,"abstract":"Notebooks provide an interactive environment for programmers to develop code, analyse data and inject interleaved visualisations in a single environment. Despite their flexibility, a major pitfall that data scientists encounter is unexpected behaviour caused by the unique out-of-order execution model of notebooks. As a result, data scientists face various challenges ranging from notebook correctness, reproducibility and cleaning. In this paper, we propose a framework that performs static analysis on notebooks, incorporating their unique execution semantics. Our framework is general in the sense that it accommodates a wide range of analyses, useful for various notebook use cases. We have instantiated our framework on a diverse set of analyses and have evaluated them on 2211 real world notebooks. Our evaluation demonstrates that the vast majority (98.7%) of notebooks can be analysed in less than a second, well within the time frame required by interactive notebook clients.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123483557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aishwarya Sivaraman, Rui Abreu, Andrew C. Scott, Tobi Akomolede, S. Chandra
Existing code repositories contain numerous instances of code patterns that are idiomatic ways of accomplishing a particular programming task. Sometimes, the programming language in use supports specific operators or APIs that can express the same idiomatic imperative code much more succinctly. However, those code patterns linger in repositories because the developers may be unaware of the new APIs or have not gotten around to them. Detection of idiomatic code can also point to the need for new APIs. We share our experiences in mining imperative idiomatic patterns from the Hack repo at Facebook. We found that existing techniques either cannot identify meaningful patterns from syntax trees or require test-suite-based dynamic analysis to incorporate semantic properties to mine useful patterns. The key insight of the approach proposed in this paper – Jezero – is that semantic idioms from a large codebase can be learned from canonicalized dataflow trees. We propose a scalable, lightweight static analysis-based approach to construct such a tree that is well suited to mine semantic idioms using nonparametric Bayesian methods. Our experiments with Jezero on Hack code show a clear advantage of adding canonicalized dataflow information to ASTs: Jezero was significantly more effective in finding new refactoring opportunities from unannotated legacy code than a baseline that did not have the dataflow augmentation.
{"title":"Mining Idioms in the Wild","authors":"Aishwarya Sivaraman, Rui Abreu, Andrew C. Scott, Tobi Akomolede, S. Chandra","doi":"10.1145/3510457.3513046","DOIUrl":"https://doi.org/10.1145/3510457.3513046","url":null,"abstract":"Existing code repositories contain numerous instances of code patterns that are idiomatic ways of accomplishing a particular programming task. Sometimes, the programming language in use supports specific operators or APIs that can express the same idiomatic imperative code much more succinctly. However, those code patterns linger in repositories because the developers may be unaware of the new APIs or have not gotten around to them. Detection of idiomatic code can also point to the need for new APIs. We share our experiences in mining imperative idiomatic patterns from the Hack repo at Facebook. We found that existing techniques either cannot identify meaningful patterns from syntax trees or require test-suite-based dynamic analysis to incorporate semantic properties to mine useful patterns. The key insight of the approach proposed in this paper – Jezero – is that semantic idioms from a large codebase can be learned from canonicalized dataflow trees. We propose a scalable, lightweight static analysis-based approach to construct such a tree that is well suited to mine semantic idioms using nonparametric Bayesian methods. Our experiments with Jezero on Hack code show a clear advantage of adding canonicalized dataflow information to ASTs: Jezero was significantly more effective in finding new refactoring opportunities from unannotated legacy code than a baseline that did not have the dataflow augmentation.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"116 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131236011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenjie Zhou, Seohyun Kim, V. Murali, Gareth Ari Aye
Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integration. Recently, accuracy in autocompletion prediction improved 12.8%[2] from training on a real-world dataset collected from programmers’ IDE activities. But what if the number of examples of IDE autocompletion in the target programming language is inadequate for model training? In this paper, we highlight practical reasons for this inadequacy, and make a call to action in using transfer learning to overcome the issue.
{"title":"Improving Code Autocompletion with Transfer Learning","authors":"Wenjie Zhou, Seohyun Kim, V. Murali, Gareth Ari Aye","doi":"10.1145/3510457.3513061","DOIUrl":"https://doi.org/10.1145/3510457.3513061","url":null,"abstract":"Software language models have achieved promising results predicting code completion usages, and several industry studies have described successful IDE integration. Recently, accuracy in autocompletion prediction improved 12.8%[2] from training on a real-world dataset collected from programmers’ IDE activities. But what if the number of examples of IDE autocompletion in the target programming language is inadequate for model training? In this paper, we highlight practical reasons for this inadequacy, and make a call to action in using transfer learning to overcome the issue.","PeriodicalId":119790,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117266478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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