App marketplaces are distribution platforms for mobile applications that serve as a communication channel between users and developers. These platforms allow users to write reviews about downloaded apps. Recent studies found that such reviews include information that is useful for software evolution. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this work we propose a taxonomy for classifying app reviews into categories relevant for software evolution. Additionally, we describe an experiment that investigates the performance of individual machine learning algorithms and its ensembles for automatically classifying the app reviews. We evaluated the performance of the machine learning techniques on 4550 reviews that were systematically labeled using content analysis methods. Overall, the ensembles had a better performance than the individual classifiers, with an average precision of 0.74 and 0.59 recall.
{"title":"Ensemble Methods for App Review Classification: An Approach for Software Evolution (N)","authors":"Emitzá Guzmán, Muhammad El-Haliby, B. Bruegge","doi":"10.1109/ASE.2015.88","DOIUrl":"https://doi.org/10.1109/ASE.2015.88","url":null,"abstract":"App marketplaces are distribution platforms for mobile applications that serve as a communication channel between users and developers. These platforms allow users to write reviews about downloaded apps. Recent studies found that such reviews include information that is useful for software evolution. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this work we propose a taxonomy for classifying app reviews into categories relevant for software evolution. Additionally, we describe an experiment that investigates the performance of individual machine learning algorithms and its ensembles for automatically classifying the app reviews. We evaluated the performance of the machine learning techniques on 4550 reviews that were systematically labeled using content analysis methods. Overall, the ensembles had a better performance than the individual classifiers, with an average precision of 0.74 and 0.59 recall.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"16 1","pages":"771-776"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81033102","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}
Yanzhen Zou, Ting-Wei Ye, Yangyang Lu, J. Mylopoulos, Lu Zhang
Question-oriented text retrieval, aka natural language-based text retrieval, has been widely used in software engineering. Earlier work has concluded that questions with the same keywords but different interrogatives (such as how, what) should result in different answers. But what is the difference? How to identify the right answers to a question? In this paper, we propose to investigate the "answer style" of software questions with different interrogatives. Towards this end, we build classifiers in a software text repository and propose a re-ranking approach to refine search results. The classifiers are trained by over 16,000 answers from the StackOverflow forum. Each answer is labeled accurately by its question's explicit or implicit interrogatives. We have evaluated the performance of our classifiers and the refinement of our re-ranking approach in software text retrieval. Our approach results in 13.1% and 12.6% respectively improvement with respect to text retrieval criteria nDCG@1 and nDCG@10 compared to the baseline. We also apply our approach to FAQs of 7 open source projects and show 13.2% improvement with respect to nDCG@1. The results of our experiments suggest that our approach could find answers to FAQs more precisely.
{"title":"Learning to Rank for Question-Oriented Software Text Retrieval (T)","authors":"Yanzhen Zou, Ting-Wei Ye, Yangyang Lu, J. Mylopoulos, Lu Zhang","doi":"10.1109/ASE.2015.24","DOIUrl":"https://doi.org/10.1109/ASE.2015.24","url":null,"abstract":"Question-oriented text retrieval, aka natural language-based text retrieval, has been widely used in software engineering. Earlier work has concluded that questions with the same keywords but different interrogatives (such as how, what) should result in different answers. But what is the difference? How to identify the right answers to a question? In this paper, we propose to investigate the \"answer style\" of software questions with different interrogatives. Towards this end, we build classifiers in a software text repository and propose a re-ranking approach to refine search results. The classifiers are trained by over 16,000 answers from the StackOverflow forum. Each answer is labeled accurately by its question's explicit or implicit interrogatives. We have evaluated the performance of our classifiers and the refinement of our re-ranking approach in software text retrieval. Our approach results in 13.1% and 12.6% respectively improvement with respect to text retrieval criteria nDCG@1 and nDCG@10 compared to the baseline. We also apply our approach to FAQs of 7 open source projects and show 13.2% improvement with respect to nDCG@1. The results of our experiments suggest that our approach could find answers to FAQs more precisely.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91131012","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}
RESTful API documentation is expensive to produce and maintain due to the lack of reusable tools and automated solutions. Most RESTful APIs are documented manually and the API developers are responsible for keeping the documentation up to date as the API evolves making the process both costly and error-prone. In this paper we introduce a novel technique using an HTTP proxy server that can be used to automatically generate RESTful API documentation and demonstrate SpyREST, an example implementation of the proposed technique. SpyREST uses a proxy to intercept example API calls and intelligently produces API documentation for RESTful Web APIs by processing the request and response data. Using the proposed HTTP proxy server based technique, RESTful API developers can significantly reduce the cost of producing and maintaining API documentation by replacing a large manual process with an automated process.
由于缺乏可重用工具和自动化解决方案，RESTful API文档的生成和维护成本很高。大多数RESTful API都是手动编制文档的，API开发人员负责在API发展时保持文档的更新，这使得该过程既昂贵又容易出错。在本文中，我们介绍了一种使用HTTP代理服务器的新技术，该服务器可用于自动生成RESTful API文档并演示SpyREST，这是所提出技术的一个示例实现。SpyREST使用代理来拦截示例API调用，并通过处理请求和响应数据智能地为RESTful Web API生成API文档。使用建议的基于HTTP代理服务器的技术，RESTful API开发人员可以通过用自动化流程取代大型手动流程来显著降低生成和维护API文档的成本。
{"title":"SpyREST: Automated RESTful API Documentation Using an HTTP Proxy Server (N)","authors":"S. Sohan, C. Anslow, F. Maurer","doi":"10.1109/ASE.2015.52","DOIUrl":"https://doi.org/10.1109/ASE.2015.52","url":null,"abstract":"RESTful API documentation is expensive to produce and maintain due to the lack of reusable tools and automated solutions. Most RESTful APIs are documented manually and the API developers are responsible for keeping the documentation up to date as the API evolves making the process both costly and error-prone. In this paper we introduce a novel technique using an HTTP proxy server that can be used to automatically generate RESTful API documentation and demonstrate SpyREST, an example implementation of the proposed technique. SpyREST uses a proxy to intercept example API calls and intelligently produces API documentation for RESTful Web APIs by processing the request and response data. Using the proposed HTTP proxy server based technique, RESTful API developers can significantly reduce the cost of producing and maintaining API documentation by replacing a large manual process with an automated process.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"193 1","pages":"271-276"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78324464","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}
Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previously-written code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying the Extended Boolean model, which considers the impact of both text similarity and potential APIs on code search. We deploy the backend of CodeHow as a Microsoft Azure service and implement the front-end as a Visual Studio extension. We evaluate CodeHow on a large-scale codebase consisting of 26K C# projects downloaded from GitHub. The experimental results show that when the top 1 results are inspected, CodeHow achieves a precision score of 0.794 (i.e., 79.4% of the first returned results are relevant code snippets). The results also show that CodeHow outperforms conventional code search tools. Furthermore, we perform a controlled experiment and a survey of Microsoft developers. The results confirm the usefulness and effectiveness of CodeHow in programming practices.
{"title":"CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E)","authors":"Fei Lv, Hongyu Zhang, Jian-Guang Lou, Shaowei Wang, D. Zhang, Jianjun Zhao","doi":"10.1109/ASE.2015.42","DOIUrl":"https://doi.org/10.1109/ASE.2015.42","url":null,"abstract":"Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previously-written code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying the Extended Boolean model, which considers the impact of both text similarity and potential APIs on code search. We deploy the backend of CodeHow as a Microsoft Azure service and implement the front-end as a Visual Studio extension. We evaluate CodeHow on a large-scale codebase consisting of 26K C# projects downloaded from GitHub. The experimental results show that when the top 1 results are inspected, CodeHow achieves a precision score of 0.794 (i.e., 79.4% of the first returned results are relevant code snippets). The results also show that CodeHow outperforms conventional code search tools. Furthermore, we perform a controlled experiment and a survey of Microsoft developers. The results confirm the usefulness and effectiveness of CodeHow in programming practices.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"19 1","pages":"260-270"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78480236","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}
Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, S. Sakti, T. Toda, Satoshi Nakamura
Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code, because pseudo-code is redundant and laborious to create. If pseudo-code could be generated automatically and instantly from given source code, we could allow for on-demand production of pseudo-code without human effort. In this paper, we propose a method to automatically generate pseudo-code from source code, specifically adopting the statistical machine translation (SMT) framework. SMT, which was originally designed to translate between two natural languages, allows us to automatically learn the relationship between source code/pseudo-code pairs, making it possible to create a pseudo-code generator with less human effort. In experiments, we generated English or Japanese pseudo-code from Python statements using SMT, and find that the generated pseudo-code is largely accurate, and aids code understanding.
{"title":"Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation (T)","authors":"Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, S. Sakti, T. Toda, Satoshi Nakamura","doi":"10.1109/ASE.2015.36","DOIUrl":"https://doi.org/10.1109/ASE.2015.36","url":null,"abstract":"Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code, because pseudo-code is redundant and laborious to create. If pseudo-code could be generated automatically and instantly from given source code, we could allow for on-demand production of pseudo-code without human effort. In this paper, we propose a method to automatically generate pseudo-code from source code, specifically adopting the statistical machine translation (SMT) framework. SMT, which was originally designed to translate between two natural languages, allows us to automatically learn the relationship between source code/pseudo-code pairs, making it possible to create a pseudo-code generator with less human effort. In experiments, we generated English or Japanese pseudo-code from Python statements using SMT, and find that the generated pseudo-code is largely accurate, and aids code understanding.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"15 1","pages":"574-584"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84785127","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}
Yoonseok Ko, Hongki Lee, Julian T Dolby, Sukyoung Ryu
We present a novel approach to analyze large-scale JavaScript applications statically by tuning the analysis scalability possibly giving up its soundness. For a given sound static baseline analysis of JavaScript programs, our framework allows users to define a sound approximation of selected executions that they are interested in analyzing, and it derives a tuned static analysis that can analyze the selected executions practically. The selected executions serve as parameters of the framework by taking trade-off between the scalability and the soundness of derived analyses. We formally describe our framework in abstract interpretation, and implement two instances of the framework. We evaluate them by analyzing large-scale real-world JavaScript applications, and the evaluation results show that the framework indeed empowers users to experiment with different levels of scalability and soundness. Our implementation provides an extra level of scalability by deriving sparse versions of derived analyses, and the implementation is publicly available.
{"title":"Practically Tunable Static Analysis Framework for Large-Scale JavaScript Applications (T)","authors":"Yoonseok Ko, Hongki Lee, Julian T Dolby, Sukyoung Ryu","doi":"10.1109/ASE.2015.28","DOIUrl":"https://doi.org/10.1109/ASE.2015.28","url":null,"abstract":"We present a novel approach to analyze large-scale JavaScript applications statically by tuning the analysis scalability possibly giving up its soundness. For a given sound static baseline analysis of JavaScript programs, our framework allows users to define a sound approximation of selected executions that they are interested in analyzing, and it derives a tuned static analysis that can analyze the selected executions practically. The selected executions serve as parameters of the framework by taking trade-off between the scalability and the soundness of derived analyses. We formally describe our framework in abstract interpretation, and implement two instances of the framework. We evaluate them by analyzing large-scale real-world JavaScript applications, and the evaluation results show that the framework indeed empowers users to experiment with different levels of scalability and soundness. Our implementation provides an extra level of scalability by deriving sparse versions of derived analyses, and the implementation is publicly available.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"54 1","pages":"541-551"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84787243","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}
Lingfeng Bao, Deheng Ye, Zhenchang Xing, Xin Xia, Xinyu Wang
Developers' daily work produces, transforms, and communicates cross-cutting information across applications, including IDEs, emails, Q&A sites, Twitter, and many others. However, these applications function independently of one another. Even though each application has their own effective information management mechanisms, cross-cutting information across separate applications creates a problem of information fragmentation, forcing developers to manually track, correlate, and re-find cross-cutting information across applications. In this paper, we present ActivitySpace, a remembrance framework that unobtrusively tracks and analyze a developer's daily work in separate applications, and provides various semantic and episodic UIs that help developers correlate and re-find cross-cutting information across applications based on information content, time and place of his/her activities. Through a user study of 8 participants, we demonstrate how ActivitySpace helps to tackle information fragmentation problem in developers' daily work.
{"title":"ActivitySpace: A Remembrance Framework to Support Interapplication Information Needs","authors":"Lingfeng Bao, Deheng Ye, Zhenchang Xing, Xin Xia, Xinyu Wang","doi":"10.1109/ASE.2015.90","DOIUrl":"https://doi.org/10.1109/ASE.2015.90","url":null,"abstract":"Developers' daily work produces, transforms, and communicates cross-cutting information across applications, including IDEs, emails, Q&A sites, Twitter, and many others. However, these applications function independently of one another. Even though each application has their own effective information management mechanisms, cross-cutting information across separate applications creates a problem of information fragmentation, forcing developers to manually track, correlate, and re-find cross-cutting information across applications. In this paper, we present ActivitySpace, a remembrance framework that unobtrusively tracks and analyze a developer's daily work in separate applications, and provides various semantic and episodic UIs that help developers correlate and re-find cross-cutting information across applications based on information content, time and place of his/her activities. Through a user study of 8 participants, we demonstrate how ActivitySpace helps to tackle information fragmentation problem in developers' daily work.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"12 1","pages":"864-869"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84937146","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}
Tests are increasingly specified as programs. Expressing tests as code is advantageous in that developers are comfortable writing and running code, and tests can be automated and reused as the software evolves. Tests expressed as code, however, can also contain faults. Some test faults are similar to those found in application code, while others are more subtle, caused by incorrect implementation of testing concepts and processes. These faults may cause a test to fail when it should not, or allow program faults to go undetected. In this work we explore whether lightweight static analyses can be cost-effective in pinpointing patterns associated with faults tests. Our exploration includes a categorization and explanation of test patterns, and their application to 12 open source projects that include over 40K tests. We found that several patterns, detectable through simple and efficient static analyses of just the test code, can detect faults with a low false positive rate, while other patterns would require a more sophisticated and extensive code analysis to be useful.
{"title":"Test Analysis: Searching for Faults in Tests (N)","authors":"M. Waterloo, S. Person, Sebastian G. Elbaum","doi":"10.1109/ASE.2015.37","DOIUrl":"https://doi.org/10.1109/ASE.2015.37","url":null,"abstract":"Tests are increasingly specified as programs. Expressing tests as code is advantageous in that developers are comfortable writing and running code, and tests can be automated and reused as the software evolves. Tests expressed as code, however, can also contain faults. Some test faults are similar to those found in application code, while others are more subtle, caused by incorrect implementation of testing concepts and processes. These faults may cause a test to fail when it should not, or allow program faults to go undetected. In this work we explore whether lightweight static analyses can be cost-effective in pinpointing patterns associated with faults tests. Our exploration includes a categorization and explanation of test patterns, and their application to 12 open source projects that include over 40K tests. We found that several patterns, detectable through simple and efficient static analyses of just the test code, can detect faults with a low false positive rate, while other patterns would require a more sophisticated and extensive code analysis to be useful.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"67 1","pages":"149-154"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82190909","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 SAFEWapp, an open-source static analysis framework for JavaScript web applications. It provides a faithful (partial) model of web application execution environments of various browsers, based on empirical data from the main web pages of the 9,465 most popular websites. A main feature of SAFEWapp is the configurability of DOM tree abstraction levels to allow users to adjust a trade-off between analysis performance and precision depending on their applications. We evaluate SAFEWapp on the 5 most popular JavaScript libraries and the main web pages of the 10 most popular websites in terms of analysis performance, precision, and modeling coverage. Additionally, as an application of SAFEWapp, we build a bug detector for JavaScript web applications that uses static analysis results from SAFEWapp. Our bug detector found previously undiscovered bugs including ones from wikipedia.org and amazon.com.
{"title":"Static Analysis of JavaScript Web Applications in the Wild via Practical DOM Modeling (T)","authors":"Changhee Park, S. Won, Joonho Jin, Sukyoung Ryu","doi":"10.1109/ASE.2015.27","DOIUrl":"https://doi.org/10.1109/ASE.2015.27","url":null,"abstract":"We present SAFEWapp, an open-source static analysis framework for JavaScript web applications. It provides a faithful (partial) model of web application execution environments of various browsers, based on empirical data from the main web pages of the 9,465 most popular websites. A main feature of SAFEWapp is the configurability of DOM tree abstraction levels to allow users to adjust a trade-off between analysis performance and precision depending on their applications. We evaluate SAFEWapp on the 5 most popular JavaScript libraries and the main web pages of the 10 most popular websites in terms of analysis performance, precision, and modeling coverage. Additionally, as an application of SAFEWapp, we build a bug detector for JavaScript web applications that uses static analysis results from SAFEWapp. Our bug detector found previously undiscovered bugs including ones from wikipedia.org and amazon.com.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"9 1","pages":"552-562"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79747856","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}
Temporal specifications, relating program events through time, are useful for tasks ranging from bug detection to program comprehension. Unfortunately, such specifications are often lacking from system descriptions, leading researchers to investigate methods for inferring these specifications from code, execution traces, code comments, and other artifacts. This paper describes Texada, a tool to dynamically mine temporal specifications in LTL from traces of program activity. We review Texada's key features and demonstrate how it can be used to investigate program behavior through two scenarios: validating an implementation that solves the dining philosophers problem and supporting comprehension of a stack implementation. We also detail Texada's other, more advanced, usage options. Texada is an open source tool: https://bitbucket.org/bestchai/texada.
{"title":"Investigating Program Behavior Using the Texada LTL Specifications Miner","authors":"Caroline Lemieux, Ivan Beschastnikh","doi":"10.1109/ASE.2015.94","DOIUrl":"https://doi.org/10.1109/ASE.2015.94","url":null,"abstract":"Temporal specifications, relating program events through time, are useful for tasks ranging from bug detection to program comprehension. Unfortunately, such specifications are often lacking from system descriptions, leading researchers to investigate methods for inferring these specifications from code, execution traces, code comments, and other artifacts. This paper describes Texada, a tool to dynamically mine temporal specifications in LTL from traces of program activity. We review Texada's key features and demonstrate how it can be used to investigate program behavior through two scenarios: validating an implementation that solves the dining philosophers problem and supporting comprehension of a stack implementation. We also detail Texada's other, more advanced, usage options. Texada is an open source tool: https://bitbucket.org/bestchai/texada.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"50 1","pages":"870-875"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80780552","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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