Sebastiano Panichella, Andrea Di Sorbo, Emitzá Guzmán, C. A. Visaggio, G. Canfora, H. Gall
Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the usefulness of ARdoc in extracting important maintenance tasks for their mobile applications. Demo URL: https://youtu.be/Baf18V6sN8E Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html
Google Play、苹果App Store和Windows Phone Store都是知名的分销平台，用户可以在这些平台上下载手机应用，对它们进行评分，并对自己使用的应用发表评论。之前的研究表明，这些评论包含了重要的信息，可以帮助开发者改进他们的应用。然而，由于每天发布的大量评论，评论的非结构化性质及其质量参差不齐，分析评论是具有挑战性的。在这个演示中，我们展示了ARdoc，一个结合了三种技术的工具:(1)自然语言解析，(2)文本分析和(3)情感分析，用于自动分类应用评论中包含的有用反馈，这些反馈对于执行软件维护和发展任务很重要。我们的定量和定性分析(涉及移动专业开发人员)表明，ARdoc正确地将用户评论中对维护观点有用的反馈分类为高精度(范围在84%到89%之间)、召回率(范围在84%到89%之间)和F-Measure(范围在84%到89%之间)。在评估我们的工具时，我们研究的开发人员证实了ARdoc在为他们的移动应用程序提取重要维护任务方面的有用性。演示网址:https://youtu.be/Baf18V6sN8E演示网页:http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html
{"title":"ARdoc: app reviews development oriented classifier","authors":"Sebastiano Panichella, Andrea Di Sorbo, Emitzá Guzmán, C. A. Visaggio, G. Canfora, H. Gall","doi":"10.1145/2950290.2983938","DOIUrl":"https://doi.org/10.1145/2950290.2983938","url":null,"abstract":"Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the usefulness of ARdoc in extracting important maintenance tasks for their mobile applications. Demo URL: https://youtu.be/Baf18V6sN8E Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html","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":"83097347","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}
Programming with tasks is a promising approach to write performance portable parallel code. In this model, the programmer explicitly specifies tasks and the task parallel runtime employs work stealing to distribute tasks among threads. Similar to multithreaded programs, task parallel programs can also exhibit data races. Unfortunately, prior data race detectors for task parallel programs either run the program serially or do not handle locks, and/or detect races only in the schedule observed by the analysis. This paper proposes PTRacer, a parallel on-the-fly data race detector for task parallel programs that use locks. PTRacer detects data races not only in the observed schedule but also those that can happen in other schedules (which are permutations of the memory operations in the observed schedule) for a given input. It accomplishes the above goal by leveraging the dynamic execution graph of a task parallel execution to determine whether two accesses can happen in parallel and by maintaining constant amount of access history metadata with each distinct set of locks held for each shared memory location. To detect data races (beyond the observed schedule) in programs with branches sensitive to scheduling decisions, we propose static compiler instrumentation that records memory accesses that will be executed in the other path with simple branches. PTRacer has performance overheads similar to the state-of-the-art race detector for task parallel programs, SPD3, while detecting more races in programs with locks.
{"title":"Parallel data race detection for task parallel programs with locks","authors":"Adarsh Yoga, Santosh Nagarakatte, Aarti Gupta","doi":"10.1145/2950290.2950329","DOIUrl":"https://doi.org/10.1145/2950290.2950329","url":null,"abstract":"Programming with tasks is a promising approach to write performance portable parallel code. In this model, the programmer explicitly specifies tasks and the task parallel runtime employs work stealing to distribute tasks among threads. Similar to multithreaded programs, task parallel programs can also exhibit data races. Unfortunately, prior data race detectors for task parallel programs either run the program serially or do not handle locks, and/or detect races only in the schedule observed by the analysis. This paper proposes PTRacer, a parallel on-the-fly data race detector for task parallel programs that use locks. PTRacer detects data races not only in the observed schedule but also those that can happen in other schedules (which are permutations of the memory operations in the observed schedule) for a given input. It accomplishes the above goal by leveraging the dynamic execution graph of a task parallel execution to determine whether two accesses can happen in parallel and by maintaining constant amount of access history metadata with each distinct set of locks held for each shared memory location. To detect data races (beyond the observed schedule) in programs with branches sensitive to scheduling decisions, we propose static compiler instrumentation that records memory accesses that will be executed in the other path with simple branches. PTRacer has performance overheads similar to the state-of-the-art race detector for task parallel programs, SPD3, while detecting more races in programs with locks.","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":"83329166","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}
End-user programming, a frequently recurring dream, has thus far eluded large-scale, complex applications. Very real, hard questions stand in the way of its realization. How can its languages and tools support: (1) The development of applications with large data sets and sophisticated computation? (2) The co-development by end-users and professional developers when the complexity of an application demands it? (3) Beyond development, the maintenance, distribution, monitoring, and integration with other applications and services? We discuss our approach to these questions, as implemented in the LogicBlox Modeler. We discuss its use in developing applications for governments, major financial institutions, and large global retailers. We highlight the essential synergies between Programming Languages, Software Engineering, and Database research to achieve self-service at scale, and present open questions to which we look to the FSE community for inspirations and solutions.
{"title":"Model, execute, and deploy: answering the hard questions in end-user programming (showcase)","authors":"S. Huang","doi":"10.1145/2950290.2994158","DOIUrl":"https://doi.org/10.1145/2950290.2994158","url":null,"abstract":"End-user programming, a frequently recurring dream, has thus far eluded large-scale, complex applications. Very real, hard questions stand in the way of its realization. How can its languages and tools support: (1) The development of applications with large data sets and sophisticated computation? (2) The co-development by end-users and professional developers when the complexity of an application demands it? (3) Beyond development, the maintenance, distribution, monitoring, and integration with other applications and services? We discuss our approach to these questions, as implemented in the LogicBlox Modeler. We discuss its use in developing applications for governments, major financial institutions, and large global retailers. We highlight the essential synergies between Programming Languages, Software Engineering, and Database research to achieve self-service at scale, and present open questions to which we look to the FSE community for inspirations and solutions.","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":"72927861","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}
S. Nejati, M. Sabetzadeh, Chetan Arora, L. Briand, Felix Mandoux
An important activity in systems engineering is analyzing how a change in requirements will impact the design of a system. Performing this analysis manually is expensive, particularly for complex systems. In this paper, we propose an approach to automatically identify the impact of requirements changes on system design, when the requirements and design elements are expressed using models. We ground our approach on the Systems Modeling Language (SysML) due to SysML's increasing use in industrial applications. Our approach has two steps: For a given change, we first apply a static slicing algorithm to extract an estimated set of impacted model elements. Next, we rank the elements of the resulting set according to a quantitative measure designed to predict how likely it is for each element to be impacted. The measure is computed using Natural Language Processing (NLP) applied to the textual content of the elements. Engineers can then inspect the ranked list of elements and identify those that are actually impacted. We evaluate our approach on an industrial case study with 16 real-world requirements changes. Our results suggest that, using our approach, engineers need to inspect on average only 4.8% of the entire design in order to identify the actually-impacted elements. We further show that our results consistently improve when our analysis takes into account both structural and behavioral diagrams rather than only structural ones, and the natural-language content of the diagrams in addition to only their structural and behavioral content.
{"title":"Automated change impact analysis between SysML models of requirements and design","authors":"S. Nejati, M. Sabetzadeh, Chetan Arora, L. Briand, Felix Mandoux","doi":"10.1145/2950290.2950293","DOIUrl":"https://doi.org/10.1145/2950290.2950293","url":null,"abstract":"An important activity in systems engineering is analyzing how a change in requirements will impact the design of a system. Performing this analysis manually is expensive, particularly for complex systems. In this paper, we propose an approach to automatically identify the impact of requirements changes on system design, when the requirements and design elements are expressed using models. We ground our approach on the Systems Modeling Language (SysML) due to SysML's increasing use in industrial applications. Our approach has two steps: For a given change, we first apply a static slicing algorithm to extract an estimated set of impacted model elements. Next, we rank the elements of the resulting set according to a quantitative measure designed to predict how likely it is for each element to be impacted. The measure is computed using Natural Language Processing (NLP) applied to the textual content of the elements. Engineers can then inspect the ranked list of elements and identify those that are actually impacted. We evaluate our approach on an industrial case study with 16 real-world requirements changes. Our results suggest that, using our approach, engineers need to inspect on average only 4.8% of the entire design in order to identify the actually-impacted elements. We further show that our results consistently improve when our analysis takes into account both structural and behavioral diagrams rather than only structural ones, and the natural-language content of the diagrams in addition to only their structural and behavioral content.","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":"73193955","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}
User reluctance and context-dependent factors during information disclosure imply that people cannot always be counted on to indicate their appropriate privacy preference. This phenomenon is the well-known 'privacy paradox', which shows that users of modern technologies are constantly concerned about their privacy, but do not apply these concerns to their usage behaviour accordingly. The problem is that this mismatch between privacy concerns and the indicated privacy preference in software, is not considered when reasoning about the satisfaction of privacy requirements. This paper is a research vision that draws connections between the imprecisions in user privacy preferences, and reasoning about the satisfaction of privacy requirements. We outline the close relationship between privacy and user beliefs and uncertainties. We then propose a multi-agent framework that leverage on this relationship when reasoning about the satisfaction of privacy requirements. We anticipate that this vision will help reduce the gap between an increasingly complex information age and the software techniques needed to protect user privacy.
{"title":"Reasoning with imprecise privacy preferences","authors":"Inah Omoronyia","doi":"10.1145/2950290.2983982","DOIUrl":"https://doi.org/10.1145/2950290.2983982","url":null,"abstract":"User reluctance and context-dependent factors during information disclosure imply that people cannot always be counted on to indicate their appropriate privacy preference. This phenomenon is the well-known 'privacy paradox', which shows that users of modern technologies are constantly concerned about their privacy, but do not apply these concerns to their usage behaviour accordingly. The problem is that this mismatch between privacy concerns and the indicated privacy preference in software, is not considered when reasoning about the satisfaction of privacy requirements. This paper is a research vision that draws connections between the imprecisions in user privacy preferences, and reasoning about the satisfaction of privacy requirements. We outline the close relationship between privacy and user beliefs and uncertainties. We then propose a multi-agent framework that leverage on this relationship when reasoning about the satisfaction of privacy requirements. We anticipate that this vision will help reduce the gap between an increasingly complex information age and the software techniques needed to protect user privacy.","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":"78205076","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}
JavaScript has become the most popular language used by developers for client and server side programming. The language, however, still lacks proper support in the form of warnings about potential bugs in the code. Most bug finding tools in use today cover bug patterns that are discovered by reading best practices or through developer intuition and anecdotal observation. As such, it is still unclear which bugs happen frequently in practice and which are important for developers to be fixed. We propose a novel semi-automatic technique, called BugAID, for discovering the most prevalent and detectable bug patterns. BugAID is based on unsupervised machine learning using language-construct-based changes distilled from AST differencing of bug fixes in the code. We present a large-scale study of common bug patterns by mining 105K commits from 134 server-side JavaScript projects. We discover 219 bug fixing change types and discuss 13 pervasive bug patterns that occur across multiple projects and can likely be prevented with better tool support. Our findings are useful for improving tools and techniques to prevent common bugs in JavaScript, guiding tool integration for IDEs, and making developers aware of common mistakes involved with programming in JavaScript.
{"title":"Discovering bug patterns in JavaScript","authors":"Quinn Hanam, Fernando Brito, A. Mesbah","doi":"10.1145/2950290.2950308","DOIUrl":"https://doi.org/10.1145/2950290.2950308","url":null,"abstract":"JavaScript has become the most popular language used by developers for client and server side programming. The language, however, still lacks proper support in the form of warnings about potential bugs in the code. Most bug finding tools in use today cover bug patterns that are discovered by reading best practices or through developer intuition and anecdotal observation. As such, it is still unclear which bugs happen frequently in practice and which are important for developers to be fixed. We propose a novel semi-automatic technique, called BugAID, for discovering the most prevalent and detectable bug patterns. BugAID is based on unsupervised machine learning using language-construct-based changes distilled from AST differencing of bug fixes in the code. We present a large-scale study of common bug patterns by mining 105K commits from 134 server-side JavaScript projects. We discover 219 bug fixing change types and discuss 13 pervasive bug patterns that occur across multiple projects and can likely be prevented with better tool support. Our findings are useful for improving tools and techniques to prevent common bugs in JavaScript, guiding tool integration for IDEs, and making developers aware of common mistakes involved with programming in JavaScript.","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":"77318332","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}
In the absence of formal specifications or test oracles, automating testing is made possible by the fact that a program must satisfy certain requirements set down by the programming language. This work describes Randoop, an automatic unit test generator which checks for invariants specified by the Java API. Randoop is able to detect violations to invariants as specified by the Java API and create error tests that reveal related bugs. Randoop is also able to produce regression tests, meant to be added to regression test suites, that capture expected behavior. We discuss additional extensions that we have made to Randoop which expands its capability for the detection of violation of specified invariants. We also examine an optimization and a heuristic for making the invariant checking process more efficient.
{"title":"Discovering additional violations of Java API invariants","authors":"Waylon Huang","doi":"10.1145/2950290.2983977","DOIUrl":"https://doi.org/10.1145/2950290.2983977","url":null,"abstract":"In the absence of formal specifications or test oracles, automating testing is made possible by the fact that a program must satisfy certain requirements set down by the programming language. This work describes Randoop, an automatic unit test generator which checks for invariants specified by the Java API. Randoop is able to detect violations to invariants as specified by the Java API and create error tests that reveal related bugs. Randoop is also able to produce regression tests, meant to be added to regression test suites, that capture expected behavior. We discuss additional extensions that we have made to Randoop which expands its capability for the detection of violation of specified invariants. We also examine an optimization and a heuristic for making the invariant checking process more efficient.","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":"73760848","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}
Software engineers often use record/replay tools to enable the automated testing of web applications. Tests created in this man- ner can then be used to regression test new versions of the web applications as they evolve. Web application tests recorded by record/replay tools, however, can be quite brittle; they can easily break as applications change. For this reason, researchers have be- gun to seek approaches for automatically repairing record/replay tests. This research investigates different aspects in relation to test- ing web applications using record/replay tools. The areas that we are interested in include taxonomizing the causes behind breakages and developing automated techniques to repair breakages, creating prevention techniques to stop the occurrence of breakages and de- veloping automated frameworks for root cause analysis. Finally, we intend to evaluate all of these activities via controlled studies involving software engineers and real web application tests.
{"title":"Regression testing of web applications using Record/Replay tools","authors":"Mouna Hammoudi","doi":"10.1145/2950290.2983942","DOIUrl":"https://doi.org/10.1145/2950290.2983942","url":null,"abstract":"Software engineers often use record/replay tools to enable the automated testing of web applications. Tests created in this man- ner can then be used to regression test new versions of the web applications as they evolve. Web application tests recorded by record/replay tools, however, can be quite brittle; they can easily break as applications change. For this reason, researchers have be- gun to seek approaches for automatically repairing record/replay tests. This research investigates different aspects in relation to test- ing web applications using record/replay tools. The areas that we are interested in include taxonomizing the causes behind breakages and developing automated techniques to repair breakages, creating prevention techniques to stop the occurrence of breakages and de- veloping automated frameworks for root cause analysis. Finally, we intend to evaluate all of these activities via controlled studies involving software engineers and real web application tests.","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":"74600977","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}
Xiaofei Xie, Bihuan Chen, Yang Liu, Wei Le, Xiaohong Li
Loops are challenging structures for program analysis, especially when loops contain multiple paths with complex interleaving executions among these paths. In this paper, we first propose a classification of multi-path loops to understand the complexity of the loop execution, which is based on the variable updates on the loop conditions and the execution order of the loop paths. Secondly, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest as inputs and summarizes path-sensitive loop effects on the variables. The key contribution is to use a path dependency automaton (PDA) to capture the execution dependency between the paths. A DFS-based algorithm is proposed to traverse the PDA to summarize the effect for all feasible executions in the loop. The experimental results show that Proteus is effective in three applications: Proteus can 1) compute a more precise bound than the existing loop bound analysis techniques; 2) significantly outperform state-of-the-art tools for loop verification; and 3) generate test cases for deep loops within one second, while KLEE and Pex either need much more time or fail.
{"title":"Proteus: computing disjunctive loop summary via path dependency analysis","authors":"Xiaofei Xie, Bihuan Chen, Yang Liu, Wei Le, Xiaohong Li","doi":"10.1145/2950290.2950340","DOIUrl":"https://doi.org/10.1145/2950290.2950340","url":null,"abstract":"Loops are challenging structures for program analysis, especially when loops contain multiple paths with complex interleaving executions among these paths. In this paper, we first propose a classification of multi-path loops to understand the complexity of the loop execution, which is based on the variable updates on the loop conditions and the execution order of the loop paths. Secondly, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest as inputs and summarizes path-sensitive loop effects on the variables. The key contribution is to use a path dependency automaton (PDA) to capture the execution dependency between the paths. A DFS-based algorithm is proposed to traverse the PDA to summarize the effect for all feasible executions in the loop. The experimental results show that Proteus is effective in three applications: Proteus can 1) compute a more precise bound than the existing loop bound analysis techniques; 2) significantly outperform state-of-the-art tools for loop verification; and 3) generate test cases for deep loops within one second, while KLEE and Pex either need much more time or fail.","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":"73462939","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}
Python is a popular dynamic language that allows quick software development. However, Python program analysis engines are largely lacking. In this paper, we present a Python predictive analysis. It first collects the trace of an execution, and then encodes the trace and unexecuted branches to symbolic constraints. Symbolic variables are introduced to denote input values, their dynamic types, and attribute sets, to reason about their variations. Solving the constraints identifies bugs and their triggering inputs. Our evaluation shows that the technique is highly effective in analyzing real-world complex programs with a lot of dynamic features and external library calls, due to its sophisticated encoding design based on traces. It identifies 46 bugs from 11 real-world projects, with 16 new bugs. All reported bugs are true positives.
{"title":"Python predictive analysis for bug detection","authors":"Zhaogui Xu, Peng Liu, X. Zhang, Baowen Xu","doi":"10.1145/2950290.2950357","DOIUrl":"https://doi.org/10.1145/2950290.2950357","url":null,"abstract":"Python is a popular dynamic language that allows quick software development. However, Python program analysis engines are largely lacking. In this paper, we present a Python predictive analysis. It first collects the trace of an execution, and then encodes the trace and unexecuted branches to symbolic constraints. Symbolic variables are introduced to denote input values, their dynamic types, and attribute sets, to reason about their variations. Solving the constraints identifies bugs and their triggering inputs. Our evaluation shows that the technique is highly effective in analyzing real-world complex programs with a lot of dynamic features and external library calls, due to its sophisticated encoding design based on traces. It identifies 46 bugs from 11 real-world projects, with 16 new bugs. All reported bugs are true positives.","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":"73619122","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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