Zhifei Chen, Bihuan Chen, Lu Xiao, Xiao Wang, Lin Chen, Yang Liu, Baowen Xu
Performance problems widely exist in modern software systems. Existing performance optimization techniques, including profiling-based and pattern-based techniques, usually fail to consider the architectural impacts among methods that easily slow down the overall system performance. This paper contributes a new approach, named Speedoo, to identify groups of methods that should be treated together and deserve high priorities for performance optimization. The uniqueness of Speedoo is to measure and rank the performance optimization opportunities of a method based on 1) the architectural impact and 2) the optimization potential. For each highly ranked method, we locate a respective Optimization Space based on 5 performance patterns generalized from empirical observations. The top ranked optimization spaces are suggested to developers as potential optimization opportunities. Our evaluation on three real-life projects has demonstrated that 18.52% to 42.86% of methods in the top ranked optimization spaces indeed undertook performance optimization in the projects. This outperforms one of the state-of-the-art profiling tools YourKit by 2 to 3 times. An important implication of this study is that developers should treat methods in an optimization space together as a group rather than as individuals in performance optimization. The proposed approach can provide guidelines and reduce developers' manual effort.
{"title":"Speedoo: Prioritizing Performance Optimization Opportunities","authors":"Zhifei Chen, Bihuan Chen, Lu Xiao, Xiao Wang, Lin Chen, Yang Liu, Baowen Xu","doi":"10.1145/3180155.3180229","DOIUrl":"https://doi.org/10.1145/3180155.3180229","url":null,"abstract":"Performance problems widely exist in modern software systems. Existing performance optimization techniques, including profiling-based and pattern-based techniques, usually fail to consider the architectural impacts among methods that easily slow down the overall system performance. This paper contributes a new approach, named Speedoo, to identify groups of methods that should be treated together and deserve high priorities for performance optimization. The uniqueness of Speedoo is to measure and rank the performance optimization opportunities of a method based on 1) the architectural impact and 2) the optimization potential. For each highly ranked method, we locate a respective Optimization Space based on 5 performance patterns generalized from empirical observations. The top ranked optimization spaces are suggested to developers as potential optimization opportunities. Our evaluation on three real-life projects has demonstrated that 18.52% to 42.86% of methods in the top ranked optimization spaces indeed undertook performance optimization in the projects. This outperforms one of the state-of-the-art profiling tools YourKit by 2 to 3 times. An important implication of this study is that developers should treat methods in an optimization space together as a group rather than as individuals in performance optimization. The proposed approach can provide guidelines and reduce developers' manual effort.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"26 1","pages":"811-821"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81245534","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}
Catarina Gralha, D. Damian, A. Wasserman, M. Goulão, João Araújo
We use Grounded Theory to study the evolution of requirements practices of 16 software startups as they grow and introduce new products and services. These startups operate in a dynamic environment, with significant time and market pressure, and rarely have time for systematic requirements analysis. Our theory describes the evolution of practice along six dimensions that emerged as relevant to their requirements activities: requirements artefacts, knowledge management, requirements-related roles, planning, technical debt and product quality. Beyond the relationships among the dimensions, our theory also explains the turning points that drove the evolution along these dimensions. These changes are reactive, rather than planned, suggesting an overall pragmatic lightness, i.e., flexibility, in the startups' evolution towards engineering practices for requirements. Our theory organises knowledge about evolving requirements practice in maturing startups, and provides practical insights for startups' assessing their own evolution as they face challenges to their growth. Our research also suggests that a startup's evolution along the six dimensions is not fundamental to its success, but has significant effects on their product, their employees and the company.
{"title":"The Evolution of Requirements Practices in Software Startups","authors":"Catarina Gralha, D. Damian, A. Wasserman, M. Goulão, João Araújo","doi":"10.1145/3180155.3180158","DOIUrl":"https://doi.org/10.1145/3180155.3180158","url":null,"abstract":"We use Grounded Theory to study the evolution of requirements practices of 16 software startups as they grow and introduce new products and services. These startups operate in a dynamic environment, with significant time and market pressure, and rarely have time for systematic requirements analysis. Our theory describes the evolution of practice along six dimensions that emerged as relevant to their requirements activities: requirements artefacts, knowledge management, requirements-related roles, planning, technical debt and product quality. Beyond the relationships among the dimensions, our theory also explains the turning points that drove the evolution along these dimensions. These changes are reactive, rather than planned, suggesting an overall pragmatic lightness, i.e., flexibility, in the startups' evolution towards engineering practices for requirements. Our theory organises knowledge about evolving requirements practice in maturing startups, and provides practical insights for startups' assessing their own evolution as they face challenges to their growth. Our research also suggests that a startup's evolution along the six dimensions is not fundamental to its success, but has significant effects on their product, their employees and the company.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"157 1","pages":"823-833"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86344810","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}
Breno Miranda, Emilio Cruciani, R. Verdecchia, A. Bertolino
Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.
{"title":"FAST Approaches to Scalable Similarity-Based Test Case Prioritization","authors":"Breno Miranda, Emilio Cruciani, R. Verdecchia, A. Bertolino","doi":"10.1145/3180155.3180210","DOIUrl":"https://doi.org/10.1145/3180155.3180210","url":null,"abstract":"Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"52 1","pages":"222-232"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85674237","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}
Leen Lambers, D. Strüber, G. Taentzer, K. Born, J. Huebert
Conflict and dependency analysis (CDA) of graph transformation has been shown to be a versatile foundation for understanding interactions in many software engineering domains, including software analysis and design, model-driven engineering, and testing. In this paper, we propose a novel static CDA technique that is multi-granular in the sense that it can detect all conflicts and dependencies on multiple granularity levels. Specifically, we provide an efficient algorithm suite for computing binary, coarse-grained, and fine-grained conflicts and dependencies: Binary granularity indicates the presence or absence of conflicts and dependencies, coarse granularity focuses on root causes for conflicts and dependencies, and fine granularity shows each conflict and dependency in full detail. Doing so, we can address specific performance and usability requirements that we identified in a literature survey of CDA usage scenarios. In an experimental evaluation, our algorithm suite computes conflicts and dependencies rapidly. Finally, we present a user study, in which the participants found our coarse-grained results more understandable than the fine-grained ones reported in a state-of-the-art tool. Our overall contribution is twofold: (i) we significantly speed up the computation of fine-grained and binary CDA results and, (ii) complement them with coarse-grained ones, which offer usability benefits for numerous use cases.
{"title":"Multi-granular Conflict and Dependency Analysis in Software Engineering Based on Graph Transformation","authors":"Leen Lambers, D. Strüber, G. Taentzer, K. Born, J. Huebert","doi":"10.1145/3180155.3180258","DOIUrl":"https://doi.org/10.1145/3180155.3180258","url":null,"abstract":"Conflict and dependency analysis (CDA) of graph transformation has been shown to be a versatile foundation for understanding interactions in many software engineering domains, including software analysis and design, model-driven engineering, and testing. In this paper, we propose a novel static CDA technique that is multi-granular in the sense that it can detect all conflicts and dependencies on multiple granularity levels. Specifically, we provide an efficient algorithm suite for computing binary, coarse-grained, and fine-grained conflicts and dependencies: Binary granularity indicates the presence or absence of conflicts and dependencies, coarse granularity focuses on root causes for conflicts and dependencies, and fine granularity shows each conflict and dependency in full detail. Doing so, we can address specific performance and usability requirements that we identified in a literature survey of CDA usage scenarios. In an experimental evaluation, our algorithm suite computes conflicts and dependencies rapidly. Finally, we present a user study, in which the participants found our coarse-grained results more understandable than the fine-grained ones reported in a state-of-the-art tool. Our overall contribution is twofold: (i) we significantly speed up the computation of fine-grained and binary CDA results and, (ii) complement them with coarse-grained ones, which offer usability benefits for numerous use cases.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"10 1","pages":"716-727"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79696837","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}
Use-After-Free (UAF) vulnerabilities are caused by the program operating on a dangling pointer and can be exploited to compromise critical software systems. While there have been many tools to mitigate UAF vulnerabilities, UAF remains one of the most common attack vectors. UAF is particularly di cult to detect in concurrent programs, in which a UAF may only occur with rare thread schedules. In this paper, we present a novel technique, UFO, that can precisely predict UAFs based on a single observed execution trace with a provably higher detection capability than existing techniques with no false positives. The key technical advancement of UFO is an extended maximal thread causality model that captures the largest possible set of feasible traces that can be inferred from a given multithreaded execution trace. By formulating UAF detection as a constraint solving problem atop this model, we can explore a much larger thread scheduling space than classical happens-before based techniques. We have evaluated UFO on several real-world large complex C/C++ programs including Chromium and FireFox. UFO scales to real-world systems with hundreds of millions of events in their execution and has detected a large number of real concurrency UAFs.
{"title":"UFO: Predictive Concurrency Use-After-Free Detection","authors":"Jeff Huang","doi":"10.1145/3180155.3180225","DOIUrl":"https://doi.org/10.1145/3180155.3180225","url":null,"abstract":"Use-After-Free (UAF) vulnerabilities are caused by the program operating on a dangling pointer and can be exploited to compromise critical software systems. While there have been many tools to mitigate UAF vulnerabilities, UAF remains one of the most common attack vectors. UAF is particularly di cult to detect in concurrent programs, in which a UAF may only occur with rare thread schedules. In this paper, we present a novel technique, UFO, that can precisely predict UAFs based on a single observed execution trace with a provably higher detection capability than existing techniques with no false positives. The key technical advancement of UFO is an extended maximal thread causality model that captures the largest possible set of feasible traces that can be inferred from a given multithreaded execution trace. By formulating UAF detection as a constraint solving problem atop this model, we can explore a much larger thread scheduling space than classical happens-before based techniques. We have evaluated UFO on several real-world large complex C/C++ programs including Chromium and FireFox. UFO scales to real-world systems with hundreds of millions of events in their execution and has detected a large number of real concurrency UAFs.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"11 1","pages":"609-619"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81904680","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}
Pengcheng Wang, Jeffrey Svajlenko, Yanzhao Wu, Yun Xu, C. Roy
Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.
{"title":"CCAligner: A Token Based Large-Gap Clone Detector","authors":"Pengcheng Wang, Jeffrey Svajlenko, Yanzhao Wu, Yun Xu, C. Roy","doi":"10.1145/3180155.3180179","DOIUrl":"https://doi.org/10.1145/3180155.3180179","url":null,"abstract":"Copying code and then pasting with large number of edits is a common activity in software development, and the pasted code is a kind of complicated Type-3 clone. Due to large number of edits, we consider the clone as a large-gap clone. Large-gap clone can reflect the extension of code, such as change and improvement. The existing state-of-the-art clone detectors suffer from several limitations in detecting large-gap clones. In this paper, we propose a tool, CCAligner, using code window that considers e edit distance for matching to detect large-gap clones. In our approach, a novel e-mismatch index is designed and the asymmetric similarity coefficient is used for similarity measure. We thoroughly evaluate CCAligner both for large-gap clone detection, and for general Type-1, Type-2 and Type-3 clone detection. The results show that CCAligner performs better than other competing tools in large-gap clone detection, and has the best execution time for 10MLOC input with good precision and recall in general Type-1 to Type-3 clone detection. Compared with existing state-of-the-art tools, CCAligner is the best performing large-gap clone detection tool, and remains competitive with the best clone detectors in general Type-1, Type-2 and Type-3 clone detection.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"47 1","pages":"1066-1077"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74521572","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}
Christopher J. Mendez, Hema Susmita Padala, Zoe Steine-Hanson, C. Hilderbrand, Amber Horvath, Charles Hill, L. Simpson, Nupoor Patil, A. Sarma, M. Burnett
Research has revealed that significant barriers exist when entering Open-Source Software (OSS) communities and that women disproportionately experience such barriers. However, this research has focused mainly on social/cultural factors, ignoring the environment itself — the tools and infrastructure. To shed some light onto how tools and infrastructure might somehow factor into OSS barriers to entry, we conducted a field study with five teams of software professionals, who worked through five use-cases to analyze the tools and infrastructure used in their OSS projects. These software professionals found tool/infrastructure barriers in 7% to 71% of the use-case steps that they analyzed, most of which are tied to newcomer barriers that have been established in the literature. Further, over 80% of the barrier types they found include attributes that are biased against women.
{"title":"Open Source Barriers to Entry, Revisited: A Sociotechnical Perspective","authors":"Christopher J. Mendez, Hema Susmita Padala, Zoe Steine-Hanson, C. Hilderbrand, Amber Horvath, Charles Hill, L. Simpson, Nupoor Patil, A. Sarma, M. Burnett","doi":"10.1145/3180155.3180241","DOIUrl":"https://doi.org/10.1145/3180155.3180241","url":null,"abstract":"Research has revealed that significant barriers exist when entering Open-Source Software (OSS) communities and that women disproportionately experience such barriers. However, this research has focused mainly on social/cultural factors, ignoring the environment itself — the tools and infrastructure. To shed some light onto how tools and infrastructure might somehow factor into OSS barriers to entry, we conducted a field study with five teams of software professionals, who worked through five use-cases to analyze the tools and infrastructure used in their OSS projects. These software professionals found tool/infrastructure barriers in 7% to 71% of the use-case steps that they analyzed, most of which are tied to newcomer barriers that have been established in the literature. Further, over 80% of the barrier types they found include attributes that are biased against women.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"41 1","pages":"1004-1015"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79064628","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}
We present a technique to automatically generate search heuristics for concolic testing. A key challenge in concolic testing is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Concolic testing employs a search heuristic to address this challenge, which favors exploring particular types of paths that are most likely to maximize the final coverage. However, manually designing a good search heuristic is nontrivial and typically ends up with suboptimal and unstable outcomes. The goal of this paper is to overcome this shortcoming of concolic testing by automatically generating search heuristics. We define a class of search heuristics, namely a parameterized heuristic, and present an algorithm that efficiently finds an optimal heuristic for each subject program. Experimental results with open-source C programs show that our technique successfully generates search heuristics that significantly outperform existing manually-crafted heuristics in terms of branch coverage and bug-finding.
{"title":"Automatically Generating Search Heuristics for Concolic Testing","authors":"Sooyoung Cha, Seongjoon Hong, Junhee Lee, Hakjoo Oh","doi":"10.1145/3180155.3180166","DOIUrl":"https://doi.org/10.1145/3180155.3180166","url":null,"abstract":"We present a technique to automatically generate search heuristics for concolic testing. A key challenge in concolic testing is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Concolic testing employs a search heuristic to address this challenge, which favors exploring particular types of paths that are most likely to maximize the final coverage. However, manually designing a good search heuristic is nontrivial and typically ends up with suboptimal and unstable outcomes. The goal of this paper is to overcome this shortcoming of concolic testing by automatically generating search heuristics. We define a class of search heuristics, namely a parameterized heuristic, and present an algorithm that efficiently finds an optimal heuristic for each subject program. Experimental results with open-source C programs show that our technique successfully generates search heuristics that significantly outperform existing manually-crafted heuristics in terms of branch coverage and bug-finding.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"10 1","pages":"1244-1254"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74248160","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}
Tianyi Zhang, Ganesha Upadhyaya, Anastasia Reinhardt, Hridesh Rajan, Miryung Kim
Programmers often consult an online Q&A forum such as Stack Overflow to learn new APIs. This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow. To reduce manual assessment effort, we design ExampleCheck, an API usage mining framework that extracts patterns from over 380K Java repositories on GitHub and subsequently reports potential API usage violations in Stack Overflow posts. We analyze 217,818 Stack Overflow posts using ExampleCheck and find that 31% may have potential API usage violations that could produce unexpected behavior such as program crashes and resource leaks. Such API misuse is caused by three main reasons—missing control constructs, missing or incorrect order of API calls, and incorrect guard conditions. Even the posts that are accepted as correct answers or upvoted by other programmers are not necessarily more reliable than other posts in terms of API misuse. This study result calls for a new approach to augment Stack Overflow with alternative API usage details that are not typically shown in curated examples.
{"title":"Are Code Examples on an Online Q&A Forum Reliable?: A Study of API Misuse on Stack Overflow","authors":"Tianyi Zhang, Ganesha Upadhyaya, Anastasia Reinhardt, Hridesh Rajan, Miryung Kim","doi":"10.1145/3180155.3180260","DOIUrl":"https://doi.org/10.1145/3180155.3180260","url":null,"abstract":"Programmers often consult an online Q&A forum such as Stack Overflow to learn new APIs. This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow. To reduce manual assessment effort, we design ExampleCheck, an API usage mining framework that extracts patterns from over 380K Java repositories on GitHub and subsequently reports potential API usage violations in Stack Overflow posts. We analyze 217,818 Stack Overflow posts using ExampleCheck and find that 31% may have potential API usage violations that could produce unexpected behavior such as program crashes and resource leaks. Such API misuse is caused by three main reasons—missing control constructs, missing or incorrect order of API calls, and incorrect guard conditions. Even the posts that are accepted as correct answers or upvoted by other programmers are not necessarily more reliable than other posts in terms of API misuse. This study result calls for a new approach to augment Stack Overflow with alternative API usage details that are not typically shown in curated examples.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"20 1","pages":"886-896"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78586293","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}
Ming Wen, Junjie Chen, Rongxin Wu, Dan Hao, S. Cheung
The effectiveness of search-based automated program repair is limited in the number of correct patches that can be successfully generated. There are two causes of such limitation. First, the search space does not contain the correct patch. Second, the search space is huge and therefore the correct patch cannot be generated (ie correct patches are either generated after incorrect plausible ones or not generated within the time budget). To increase the likelihood of including the correct patches in the search space, we propose to work at a fine granularity in terms of AST nodes. This, however, will further enlarge the search space, increasing the challenge to find the correct patches. We address the challenge by devising a strategy to prioritize the candidate patches based on their likelihood of being correct. Specifically, we study the use of AST nodes' context information to estimate the likelihood. In this paper, we propose CapGen, a context-aware patch generation technique. The novelty which allows CapGen to produce more correct patches lies in three aspects: (1) The fine-granularity design enables it to find more correct fixing ingredients; (2) The context-aware prioritization of mutation operators enables it to constrain the search space; (3) Three context-aware models enable it to rank correct patches at high positions before incorrect plausible ones. We evaluate CapGen on Defects4J and compare it with the state-of-the-art program repair techniques. Our evaluation shows that CapGen outperforms and complements existing techniques. CapGen achieves a high precision of 84.00% and can prioritize the correct patches before 98.78% of the incorrect plausible ones.
{"title":"Context-Aware Patch Generation for Better Automated Program Repair","authors":"Ming Wen, Junjie Chen, Rongxin Wu, Dan Hao, S. Cheung","doi":"10.1145/3180155.3180233","DOIUrl":"https://doi.org/10.1145/3180155.3180233","url":null,"abstract":"The effectiveness of search-based automated program repair is limited in the number of correct patches that can be successfully generated. There are two causes of such limitation. First, the search space does not contain the correct patch. Second, the search space is huge and therefore the correct patch cannot be generated (ie correct patches are either generated after incorrect plausible ones or not generated within the time budget). To increase the likelihood of including the correct patches in the search space, we propose to work at a fine granularity in terms of AST nodes. This, however, will further enlarge the search space, increasing the challenge to find the correct patches. We address the challenge by devising a strategy to prioritize the candidate patches based on their likelihood of being correct. Specifically, we study the use of AST nodes' context information to estimate the likelihood. In this paper, we propose CapGen, a context-aware patch generation technique. The novelty which allows CapGen to produce more correct patches lies in three aspects: (1) The fine-granularity design enables it to find more correct fixing ingredients; (2) The context-aware prioritization of mutation operators enables it to constrain the search space; (3) Three context-aware models enable it to rank correct patches at high positions before incorrect plausible ones. We evaluate CapGen on Defects4J and compare it with the state-of-the-art program repair techniques. Our evaluation shows that CapGen outperforms and complements existing techniques. CapGen achieves a high precision of 84.00% and can prioritize the correct patches before 98.78% of the incorrect plausible ones.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"1 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85447464","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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