Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115630
Ahmet Çelik, Karl Palmskog, Miloš Gligorić
Proof assistants such as Coq are used to construct and check formal proofs in many large-scale verification projects. As proofs grow in number and size, the need for tool support to quickly find failing proofs after revising a project increases. We present a technique for large-scale regression proof selection, suitable for use in continuous integration services, e.g., Travis CI. We instantiate the technique in a tool dubbed iCoq. iCoq tracks fine-grained dependencies between Coq definitions, propositions, and proofs, and only checks those proofs affected by changes between two revisions. iCoq additionally saves time by ignoring changes with no impact on semantics. We applied iCoq to track dependencies across many revisions in several large Coq projects and measured the time savings compared to proof checking from scratch and when using Coq's timestamp-based toolchain for incremental checking. Our results show that proof checking with iC oq is up to 10 times faster than the former and up to 3 times faster than the latter.
{"title":"ICoq: Regression proof selection for large-scale verification projects","authors":"Ahmet Çelik, Karl Palmskog, Miloš Gligorić","doi":"10.1109/ASE.2017.8115630","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115630","url":null,"abstract":"Proof assistants such as Coq are used to construct and check formal proofs in many large-scale verification projects. As proofs grow in number and size, the need for tool support to quickly find failing proofs after revising a project increases. We present a technique for large-scale regression proof selection, suitable for use in continuous integration services, e.g., Travis CI. We instantiate the technique in a tool dubbed iCoq. iCoq tracks fine-grained dependencies between Coq definitions, propositions, and proofs, and only checks those proofs affected by changes between two revisions. iCoq additionally saves time by ignoring changes with no impact on semantics. We applied iCoq to track dependencies across many revisions in several large Coq projects and measured the time savings compared to proof checking from scratch and when using Coq's timestamp-based toolchain for incremental checking. Our results show that proof checking with iC oq is up to 10 times faster than the former and up to 3 times faster than the latter.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124357853","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115713
M. Asaduzzaman, C. Roy, Kevin A. Schneider, Daqing Hou
Software frameworks enable developers to reuse existing well tested functionalities instead of taking the burden of implementing everything from scratch. However, to meet application specific requirements, the frameworks need to be customized via extension points. This is often done by passing a framework related object as an argument to an API call. To enable such customizations, the object can be created by extending a framework class, implementing an interface, or changing the properties of the object via API calls. However, it is both a common and non-trivial task to find all the details related to the customizations. In this paper, we present a tool, called FEMIR, that utilizes partial program analysis and graph mining technique to detect, group, and rank framework extension examples. The tool extends existing code completion infrastructure to inform developers about customization choices, enabling them to browse through extension points of a framework, and frequent usages of each point in terms of code examples. A video demo is made available at https://asaduzzamanparvez.wordpress.com/femir.
{"title":"FEMIR: A tool for recommending framework extension examples","authors":"M. Asaduzzaman, C. Roy, Kevin A. Schneider, Daqing Hou","doi":"10.1109/ASE.2017.8115713","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115713","url":null,"abstract":"Software frameworks enable developers to reuse existing well tested functionalities instead of taking the burden of implementing everything from scratch. However, to meet application specific requirements, the frameworks need to be customized via extension points. This is often done by passing a framework related object as an argument to an API call. To enable such customizations, the object can be created by extending a framework class, implementing an interface, or changing the properties of the object via API calls. However, it is both a common and non-trivial task to find all the details related to the customizations. In this paper, we present a tool, called FEMIR, that utilizes partial program analysis and graph mining technique to detect, group, and rank framework extension examples. The tool extends existing code completion infrastructure to inform developers about customization choices, enabling them to browse through extension points of a framework, and frequent usages of each point in terms of code examples. A video demo is made available at https://asaduzzamanparvez.wordpress.com/femir.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117146501","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115645
Shuai Wang, Dinghao Wu
Detecting similar functions in binary executables serves as a foundation for many binary code analysis and reuse tasks. By far, recognizing similar components in binary code remains a challenge. Existing research employs either static or dynamic approaches to capture program syntax or semantics-level features for comparison. However, there exist multiple design limitations in previous work, which result in relatively high cost, low accuracy and scalability, and thus severely impede their practical use. In this paper, we present a novel method that leverages in-memory fuzzing for binary code similarity analysis. Our prototype tool IMF-SIM applies in-memory fuzzing to launch analysis towards every function and collect traces of different kinds of program behaviors. The similarity score of two behavior traces is computed according to their longest common subsequence. To compare two functions, a feature vector is generated, whose elements are the similarity scores of the behavior trace-level comparisons. We train a machine learning model through labeled feature vectors; later, for a given feature vector by comparing two functions, the trained model gives a final score, representing the similarity score of the two functions. We evaluate IMF-SIM against binaries compiled by different compilers, optimizations, and commonly-used obfuscation methods, in total over one thousand binary executables. Our evaluation shows that IMF-SIM notably outperforms existing tools with higher accuracy and broader application scopes.
{"title":"In-memory fuzzing for binary code similarity analysis","authors":"Shuai Wang, Dinghao Wu","doi":"10.1109/ASE.2017.8115645","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115645","url":null,"abstract":"Detecting similar functions in binary executables serves as a foundation for many binary code analysis and reuse tasks. By far, recognizing similar components in binary code remains a challenge. Existing research employs either static or dynamic approaches to capture program syntax or semantics-level features for comparison. However, there exist multiple design limitations in previous work, which result in relatively high cost, low accuracy and scalability, and thus severely impede their practical use. In this paper, we present a novel method that leverages in-memory fuzzing for binary code similarity analysis. Our prototype tool IMF-SIM applies in-memory fuzzing to launch analysis towards every function and collect traces of different kinds of program behaviors. The similarity score of two behavior traces is computed according to their longest common subsequence. To compare two functions, a feature vector is generated, whose elements are the similarity scores of the behavior trace-level comparisons. We train a machine learning model through labeled feature vectors; later, for a given feature vector by comparing two functions, the trained model gives a final score, representing the similarity score of the two functions. We evaluate IMF-SIM against binaries compiled by different compilers, optimizations, and commonly-used obfuscation methods, in total over one thousand binary executables. Our evaluation shows that IMF-SIM notably outperforms existing tools with higher accuracy and broader application scopes.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127464391","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115616
Chucheng Zhang, Haoliang Cheng, Enyi Tang, Xin Chen, Lei Bu, Xuandong Li
Mobile applications with complex GUIs are very popular today. However, generating test cases for these applications is often tedious professional work. On the one hand, manually designing and writing elaborate GUI scripts requires expertise. On the other hand, generating GUI scripts with record and playback techniques usually depends on repetitive work that testers need to interact with the application over and over again, because only one path is recorded in an execution. Automatic GUI testing focuses on exploring combinations of GUI events. As the number of combinations is huge, it is still necessary to introduce a test interface for testers to reduce its search space. This paper presents a sketch-guided GUI test generation approach for testing mobile applications, which provides a simple but expressive interface for testers to specify their testing purposes. Testers just need to draw a few simple strokes on the screenshots. Then our approach translates the strokes to a testing model and initiates a model-based automatic GUI testing. We evaluate our sketch-guided approach on a few real-world Android applications collected from the literature. The results show that our approach can achieve higher coverage than existing automatic GUI testing techniques with just 10-minute sketching for an application.
{"title":"Sketch-guided GUI test generation for mobile applications","authors":"Chucheng Zhang, Haoliang Cheng, Enyi Tang, Xin Chen, Lei Bu, Xuandong Li","doi":"10.1109/ASE.2017.8115616","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115616","url":null,"abstract":"Mobile applications with complex GUIs are very popular today. However, generating test cases for these applications is often tedious professional work. On the one hand, manually designing and writing elaborate GUI scripts requires expertise. On the other hand, generating GUI scripts with record and playback techniques usually depends on repetitive work that testers need to interact with the application over and over again, because only one path is recorded in an execution. Automatic GUI testing focuses on exploring combinations of GUI events. As the number of combinations is huge, it is still necessary to introduce a test interface for testers to reduce its search space. This paper presents a sketch-guided GUI test generation approach for testing mobile applications, which provides a simple but expressive interface for testers to specify their testing purposes. Testers just need to draw a few simple strokes on the screenshots. Then our approach translates the strokes to a testing model and initiates a model-based automatic GUI testing. We evaluate our sketch-guided approach on a few real-world Android applications collected from the literature. The results show that our approach can achieve higher coverage than existing automatic GUI testing techniques with just 10-minute sketching for an application.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124986633","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115690
Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sanán, Henri Hansen
Loop invariant generation is a fundamental problem in program analysis and verification. In this work, we propose a new approach to automatically constructing inductive loop invariants. The key idea is to aggressively squeeze an inductive invariant based on Craig interpolants between forward and backward reachability analysis. We have evaluated our approach by a set of loop benchmarks, and experimental results show that our approach is promising.
{"title":"FiB: Squeezing loop invariants by interpolation between forward/backward predicate transformers","authors":"Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sanán, Henri Hansen","doi":"10.1109/ASE.2017.8115690","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115690","url":null,"abstract":"Loop invariant generation is a fundamental problem in program analysis and verification. In this work, we propose a new approach to automatically constructing inductive loop invariants. The key idea is to aggressively squeeze an inductive invariant based on Craig interpolants between forward and backward reachability analysis. We have evaluated our approach by a set of loop benchmarks, and experimental results show that our approach is promising.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"536 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123910661","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115680
Thomas Rolfsnes, L. Moonen, D. Binkley
Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: Co-Change and Tarmaq. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.
{"title":"Predicting relevance of change recommendations","authors":"Thomas Rolfsnes, L. Moonen, D. Binkley","doi":"10.1109/ASE.2017.8115680","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115680","url":null,"abstract":"Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: Co-Change and Tarmaq. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111105","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115721
Sebastian Nielebock
The domain of Automatic Program Repair (APR) had many research contributions in recent years. So far, most approaches target fixing generic bugs in programs (e.g., off-by-one errors). Nevertheless, recent studies reveal that about 50% of real bugs require API-specific fixes (e.g., adding missing API method calls or correcting method ordering), for which existing APR approaches are not designed. In this paper, we address this problem and introduce the notion of an API-specific program repair mechanism. This mechanism detects erroneous code in a similar way to existing APR approaches. However, to fix such bugs, it uses API-specific information from the erroneous code to search for API usage patterns in other software, with which we could fix the bug. We provide first insights on the applicability of this mechanism and discuss upcoming research challenges.
{"title":"Towards API-specific automatic program repair","authors":"Sebastian Nielebock","doi":"10.1109/ASE.2017.8115721","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115721","url":null,"abstract":"The domain of Automatic Program Repair (APR) had many research contributions in recent years. So far, most approaches target fixing generic bugs in programs (e.g., off-by-one errors). Nevertheless, recent studies reveal that about 50% of real bugs require API-specific fixes (e.g., adding missing API method calls or correcting method ordering), for which existing APR approaches are not designed. In this paper, we address this problem and introduce the notion of an API-specific program repair mechanism. This mechanism detects erroneous code in a similar way to existing APR approaches. However, to fix such bugs, it uses API-specific information from the erroneous code to search for API usage patterns in other software, with which we could fix the bug. We provide first insights on the applicability of this mechanism and discuss upcoming research challenges.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957511","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115692
Alan Leung, Sorin Lerner
We present Parsimony, a programming-by-example development environment for synthesizing lexers and parsers by example. Parsimony provides a graphical interface in which the user presents examples simply by selecting and labeling sample text in a text editor. An underlying synthesis engine then constructs syntactic rules to solve the system of constraints induced by the supplied examples. Parsimony is more expressive and usable than prior programming-by-example systems for parsers in several ways: Parsimony can (1) synthesize lexer rules in addition to productions, (2) solve for much larger constraint systems over multiple examples, rather than handling examples one-at-a-time, and (3) infer much more complex sets of productions, such as entire algebraic expression grammars, by detecting instances of well-known grammar design patterns. The results of a controlled user study across 18 participants show that users are able to perform lexing and parsing tasks faster and with fewer mistakes when using Parsimony as compared to a traditional parsing workflow.
{"title":"Parsimony: An IDE for example-guided synthesis of lexers and parsers","authors":"Alan Leung, Sorin Lerner","doi":"10.1109/ASE.2017.8115692","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115692","url":null,"abstract":"We present Parsimony, a programming-by-example development environment for synthesizing lexers and parsers by example. Parsimony provides a graphical interface in which the user presents examples simply by selecting and labeling sample text in a text editor. An underlying synthesis engine then constructs syntactic rules to solve the system of constraints induced by the supplied examples. Parsimony is more expressive and usable than prior programming-by-example systems for parsers in several ways: Parsimony can (1) synthesize lexer rules in addition to productions, (2) solve for much larger constraint systems over multiple examples, rather than handling examples one-at-a-time, and (3) infer much more complex sets of productions, such as entire algebraic expression grammars, by detecting instances of well-known grammar design patterns. The results of a controlled user study across 18 participants show that users are able to perform lexing and parsing tasks faster and with fewer mistakes when using Parsimony as compared to a traditional parsing workflow.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123274907","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115643
Bo Jiang, Yuxuan Wu, Teng Li, W. Chan
To ensure the quality of Android applications, many automatic test case generation techniques have been proposed. Among them, the Monkey fuzz testing tool and its variants are simple, effective and widely applicable. However, one major drawback of those Monkey tools is that they often generate many events in a failure-inducing input trace, which makes the follow-up debugging activities hard to apply. It is desirable to simplify or reduce the input event sequence while triggering the same failure. In this paper, we propose an efficient event trace representation and the SimplyDroid tool with three hierarchical delta-debugging algorithms each operating on this trace representation to simplify crash traces. We have evaluated SimplyDroid on a suite of real-life Android applications with 92 crash traces. The empirical result shows that our new algorithms in SimplyDroid are both efficient and effective in reducing these event traces.
{"title":"SimplyDroid: Efficient event sequence simplification for android application","authors":"Bo Jiang, Yuxuan Wu, Teng Li, W. Chan","doi":"10.1109/ASE.2017.8115643","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115643","url":null,"abstract":"To ensure the quality of Android applications, many automatic test case generation techniques have been proposed. Among them, the Monkey fuzz testing tool and its variants are simple, effective and widely applicable. However, one major drawback of those Monkey tools is that they often generate many events in a failure-inducing input trace, which makes the follow-up debugging activities hard to apply. It is desirable to simplify or reduce the input event sequence while triggering the same failure. In this paper, we propose an efficient event trace representation and the SimplyDroid tool with three hierarchical delta-debugging algorithms each operating on this trace representation to simplify crash traces. We have evaluated SimplyDroid on a suite of real-life Android applications with 92 crash traces. The empirical result shows that our new algorithms in SimplyDroid are both efficient and effective in reducing these event traces.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123056918","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115714
Jinfeng Lin, Yalin Liu, Jin Guo, J. Cleland-Huang, Will Goss, Wenchuang Liu, Sugandha Lohar, Natawut Monaikul, A. Rasin
Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo.
{"title":"TiQi: A natural language interface for querying software project data","authors":"Jinfeng Lin, Yalin Liu, Jin Guo, J. Cleland-Huang, Will Goss, Wenchuang Liu, Sugandha Lohar, Natawut Monaikul, A. Rasin","doi":"10.1109/ASE.2017.8115714","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115714","url":null,"abstract":"Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141352","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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