Loops are challenging structures in program analysis, and an effective loop analysis is crucial in the applications, such as symbolic execution and program verification. In the research, we will first perform a deep analysis and propose a classification according to the complexity of the loops. Then try to propose techniques for analyzing and summarizing different loops. At last, we apply the techniques in multiple applications.
{"title":"Static loop analysis and its applications","authors":"Xie Xiaofei","doi":"10.1145/2950290.2983972","DOIUrl":"https://doi.org/10.1145/2950290.2983972","url":null,"abstract":"Loops are challenging structures in program analysis, and an effective loop analysis is crucial in the applications, such as symbolic execution and program verification. In the research, we will first perform a deep analysis and propose a classification according to the complexity of the loops. Then try to propose techniques for analyzing and summarizing different loops. At last, we apply the techniques in multiple applications.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75766613","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}
Bonita Sharif, Benjamin Clark, Jonathan I. Maletic
A new research paradigm is proposed that leverages developer eye gaze to improve the state of the art in software engineering research and practice. The vision of this new paradigm for use on software engineering tasks such as code summarization, code recommendations, prediction, and continuous traceability is described. Based on this new paradigm, it is foreseen that new benchmarks will emerge based on developer gaze. The research borrows from cognitive psychology, artificial intelligence, information retrieval, and data mining. It is hypothesized that new algorithms will be discovered that work with eye gaze data to help improve current IDEs, thus improving developer productivity. Conducting empirical studies using an eye tracker will lead to inventing, evaluating, and applying innovative methods and tools that use eye gaze to support the developer. The implications and challenges of this paradigm for future software engineering research is discussed.
{"title":"Studying developer gaze to empower software engineering research and practice","authors":"Bonita Sharif, Benjamin Clark, Jonathan I. Maletic","doi":"10.1145/2950290.2983988","DOIUrl":"https://doi.org/10.1145/2950290.2983988","url":null,"abstract":"A new research paradigm is proposed that leverages developer eye gaze to improve the state of the art in software engineering research and practice. The vision of this new paradigm for use on software engineering tasks such as code summarization, code recommendations, prediction, and continuous traceability is described. Based on this new paradigm, it is foreseen that new benchmarks will emerge based on developer gaze. The research borrows from cognitive psychology, artificial intelligence, information retrieval, and data mining. It is hypothesized that new algorithms will be discovered that work with eye gaze data to help improve current IDEs, thus improving developer productivity. Conducting empirical studies using an eye tracker will lead to inventing, evaluating, and applying innovative methods and tools that use eye gaze to support the developer. The implications and challenges of this paradigm for future software engineering research is discussed.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74707977","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}
Can we teach computers how to program? Recent advances in neural network research reveal that certain neural networks are able not only to learn the syntax, grammar and semantics of arbitrary character sequences, but also synthesize new samples `in the style of' the original training data. We explore the adaptation of these techniques to code classification, comprehension and completion.
{"title":"Guided code synthesis using deep neural networks","authors":"Carol V. Alexandru","doi":"10.1145/2950290.2983951","DOIUrl":"https://doi.org/10.1145/2950290.2983951","url":null,"abstract":"Can we teach computers how to program? Recent advances in neural network research reveal that certain neural networks are able not only to learn the syntax, grammar and semantics of arbitrary character sequences, but also synthesize new samples `in the style of' the original training data. We explore the adaptation of these techniques to code classification, comprehension and completion.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72998219","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}
H. Yoshida, Susumu Tokumoto, M. Prasad, Indradeep Ghosh, T. Uehara
Automated unit test generation bears the promise of significantly reducing test cost and hence improving software quality. However, the maintenance cost of the automatically generated tests presents a significant barrier to adoption of this technology. To address this challenge, in previous work, we proposed a novel technique for automated and fine-grained incremental generation of unit tests through minimal augmentation of an existing test suite. In this paper we describe a tool FSX, implementing this technique. We describe the architecture, user-interface, and salient features of FSX, and specific practical use-cases of its technology. We also report on a real, large-scale deployment of FSX, as a practical validation of the underlying research contribution and of automated test generation research in general.
{"title":"FSX: a tool for fine-grained incremental unit test generation for C/C++ programs","authors":"H. Yoshida, Susumu Tokumoto, M. Prasad, Indradeep Ghosh, T. Uehara","doi":"10.1145/2950290.2983937","DOIUrl":"https://doi.org/10.1145/2950290.2983937","url":null,"abstract":"Automated unit test generation bears the promise of significantly reducing test cost and hence improving software quality. However, the maintenance cost of the automatically generated tests presents a significant barrier to adoption of this technology. To address this challenge, in previous work, we proposed a novel technique for automated and fine-grained incremental generation of unit tests through minimal augmentation of an existing test suite. In this paper we describe a tool FSX, implementing this technique. We describe the architecture, user-interface, and salient features of FSX, and specific practical use-cases of its technology. We also report on a real, large-scale deployment of FSX, as a practical validation of the underlying research contribution and of automated test generation research in general.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74330278","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}
Wensheng Dou, S. Cheung, Chushu Gao, Chang Xu, Liang Xu, Jun Wei
Spreadsheets are widely used by end users for various business tasks, such as data analysis and financial reporting. End users may perform similar tasks by cloning a block of cells (table) in their spreadsheets. The corresponding cells in these cloned tables are supposed to keep the same or similar computational semantics. However, when spreadsheets evolve, thus cloned tables can become inconsistent due to ad-hoc modifications, and as a result suffer from smells. In this paper, we propose TableCheck to detect table clones and related smells due to inconsistency among them. We observe that two tables with the same header information at their corresponding cells are likely to be table clones. Inspired by existing fingerprint-based code clone detection techniques, we developed a detection algorithm to detect this kind of table clones. We further detected outliers among corresponding cells as smells in the detected table clones. We implemented our idea into TableCheck, and applied it to real-world spreadsheets from the EUSES corpus. Experimental results show that table clones commonly exist (21.8%), and 25.6% of the spreadsheets with table clones suffer from smells due to inconsistency among these clones. TableCheck detected table clones and their smells with a precision of 92.2% and 85.5%, respectively, while existing techniques detected no more than 35.6% true smells that TableCheck could detect.
{"title":"Detecting table clones and smells in spreadsheets","authors":"Wensheng Dou, S. Cheung, Chushu Gao, Chang Xu, Liang Xu, Jun Wei","doi":"10.1145/2950290.2950359","DOIUrl":"https://doi.org/10.1145/2950290.2950359","url":null,"abstract":"Spreadsheets are widely used by end users for various business tasks, such as data analysis and financial reporting. End users may perform similar tasks by cloning a block of cells (table) in their spreadsheets. The corresponding cells in these cloned tables are supposed to keep the same or similar computational semantics. However, when spreadsheets evolve, thus cloned tables can become inconsistent due to ad-hoc modifications, and as a result suffer from smells. In this paper, we propose TableCheck to detect table clones and related smells due to inconsistency among them. We observe that two tables with the same header information at their corresponding cells are likely to be table clones. Inspired by existing fingerprint-based code clone detection techniques, we developed a detection algorithm to detect this kind of table clones. We further detected outliers among corresponding cells as smells in the detected table clones. We implemented our idea into TableCheck, and applied it to real-world spreadsheets from the EUSES corpus. Experimental results show that table clones commonly exist (21.8%), and 25.6% of the spreadsheets with table clones suffer from smells due to inconsistency among these clones. TableCheck detected table clones and their smells with a precision of 92.2% and 85.5%, respectively, while existing techniques detected no more than 35.6% true smells that TableCheck could detect.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85146632","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}
Brittany Johnson, Rahul Pandita, Justin Smith, Denae Ford, Sarah Elder, E. Murphy-Hill, S. Heckman, Caitlin Sadowski
Program analysis tools use notifications to communicate with developers, but previous research suggests that developers encounter challenges that impede this communication. This paper describes a qualitative study that identifies 10 kinds of challenges that cause notifications to miscommunicate with developers. Our resulting notification communication theory reveals that many challenges span multiple tools and multiple levels of developer experience. Our results suggest that, for example, future tools that model developer experience could improve communication and help developers build more accurate mental models.
{"title":"A cross-tool communication study on program analysis tool notifications","authors":"Brittany Johnson, Rahul Pandita, Justin Smith, Denae Ford, Sarah Elder, E. Murphy-Hill, S. Heckman, Caitlin Sadowski","doi":"10.1145/2950290.2950304","DOIUrl":"https://doi.org/10.1145/2950290.2950304","url":null,"abstract":"Program analysis tools use notifications to communicate with developers, but previous research suggests that developers encounter challenges that impede this communication. This paper describes a qualitative study that identifies 10 kinds of challenges that cause notifications to miscommunicate with developers. Our resulting notification communication theory reveals that many challenges span multiple tools and multiple levels of developer experience. Our results suggest that, for example, future tools that model developer experience could improve communication and help developers build more accurate mental models.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85800559","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}
Automated testing has been a focus of research for a long time. As such, we tend to think about this in a coverage centric manner. Testing budgets have also driven research such as prioritization and test selection, but as a secondary concern. As our systems get larger, are more dynamic, and impact more people with each change, we argue that we should switch from a coverage centric view to a budgeted testing centric view. Researchers in other fields have designed approximation algorithms for such budgeted scenarios and these are often simple to implement and run. In this paper we present an exemplar study on combinatorial interaction testing (CIT) to show that a budgeted greedy algorithm, when adapted to our problem for various budgets, does almost as well coverage-wise as a state of the art greedy CIT algorithm, better in some cases than a state of the art simulated annealing, and always improves over random. This suggests that we might benefit from switching our focus in large systems, from coverage to budgets.
{"title":"Budgeted testing through an algorithmic lens","authors":"Myra B. Cohen, A. Pavan, N. V. Vinodchandran","doi":"10.1145/2950290.2983987","DOIUrl":"https://doi.org/10.1145/2950290.2983987","url":null,"abstract":"Automated testing has been a focus of research for a long time. As such, we tend to think about this in a coverage centric manner. Testing budgets have also driven research such as prioritization and test selection, but as a secondary concern. As our systems get larger, are more dynamic, and impact more people with each change, we argue that we should switch from a coverage centric view to a budgeted testing centric view. Researchers in other fields have designed approximation algorithms for such budgeted scenarios and these are often simple to implement and run. In this paper we present an exemplar study on combinatorial interaction testing (CIT) to show that a budgeted greedy algorithm, when adapted to our problem for various budgets, does almost as well coverage-wise as a state of the art greedy CIT algorithm, better in some cases than a state of the art simulated annealing, and always improves over random. This suggests that we might benefit from switching our focus in large systems, from coverage to budgets.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76752869","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}
App developers would like to understand the impact of their own and their competitors’ software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings.
{"title":"Causal impact analysis for app releases in google play","authors":"William J. Martin, Federica Sarro, M. Harman","doi":"10.1145/2950290.2950320","DOIUrl":"https://doi.org/10.1145/2950290.2950320","url":null,"abstract":"App developers would like to understand the impact of their own and their competitors’ software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81314159","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}
Fang-Hsiang Su, Jonathan Bell, Kenneth Harvey, S. Sethumadhavan, G. Kaiser, T. Jebara
Detecting “similar code” is useful for many software engineering tasks. Current tools can help detect code with statically similar syntactic and–or semantic features (code clones) and with dynamically similar functional input/output (simions). Unfortunately, some code fragments that behave similarly at the finer granularity of their execution traces may be ignored. In this paper, we propose the term “code relatives” to refer to code with similar execution behavior. We define code relatives and then present DyCLINK, our approach to detecting code relatives within and across codebases. DyCLINK records instruction-level traces from sample executions, organizes the traces into instruction-level dynamic dependence graphs, and employs our specialized subgraph matching algorithm to efficiently compare the executions of candidate code relatives. In our experiments, DyCLINK analyzed 422+ million prospective subgraph matches in only 43 minutes. We compared DyCLINK to one static code clone detector from the community and to our implementation of a dynamic simion detector. The results show that DyCLINK effectively detects code relatives with a reasonable analysis time.
{"title":"Code relatives: detecting similarly behaving software","authors":"Fang-Hsiang Su, Jonathan Bell, Kenneth Harvey, S. Sethumadhavan, G. Kaiser, T. Jebara","doi":"10.1145/2950290.2950321","DOIUrl":"https://doi.org/10.1145/2950290.2950321","url":null,"abstract":"Detecting “similar code” is useful for many software engineering tasks. Current tools can help detect code with statically similar syntactic and–or semantic features (code clones) and with dynamically similar functional input/output (simions). Unfortunately, some code fragments that behave similarly at the finer granularity of their execution traces may be ignored. In this paper, we propose the term “code relatives” to refer to code with similar execution behavior. We define code relatives and then present DyCLINK, our approach to detecting code relatives within and across codebases. DyCLINK records instruction-level traces from sample executions, organizes the traces into instruction-level dynamic dependence graphs, and employs our specialized subgraph matching algorithm to efficiently compare the executions of candidate code relatives. In our experiments, DyCLINK analyzed 422+ million prospective subgraph matches in only 43 minutes. We compared DyCLINK to one static code clone detector from the community and to our implementation of a dynamic simion detector. The results show that DyCLINK effectively detects code relatives with a reasonable analysis time.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78648733","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}
This talk describes the developer workflow at Google, and our use of program analysis, testing, metrics, and tooling to reduce errors when creating and committing changes to source code. Software development at Google has several unique characteristics such as our monolithic codebase and distributed hermetic build system. Changes are vetted both manually, via our internal code review tool, and automatically, via sources such as the Tricorder program analysis platform and our automated testing infrastructure.
{"title":"Developer workflow at google (showcase)","authors":"Caitlin Sadowski","doi":"10.1145/2950290.2994156","DOIUrl":"https://doi.org/10.1145/2950290.2994156","url":null,"abstract":"This talk describes the developer workflow at Google, and our use of program analysis, testing, metrics, and tooling to reduce errors when creating and committing changes to source code. Software development at Google has several unique characteristics such as our monolithic codebase and distributed hermetic build system. Changes are vetted both manually, via our internal code review tool, and automatically, via sources such as the Tricorder program analysis platform and our automated testing infrastructure.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87116577","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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