Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115687
G. Pinto, Anthony Canino, F. C. Filho, G. Xu, Yu David Liu
ForkJoin framework is a widely used parallel programming framework upon which both core concurrency libraries and real-world applications are built. Beneath its simple and user-friendly APIs, ForkJoin is a sophisticated managed parallel runtime unfamiliar to many application programmers: the framework core is a work-stealing scheduler, handles fine-grained tasks, and sustains the pressure from automatic memory management. ForkJoin poses a unique gap in the compute stack between high-level software engineering and low-level system optimization. Understanding and bridging this gap is crucial for the future of parallelism support in JVM-supported applications. This paper describes a comprehensive study on parallelism bottlenecks in ForkJoin applications, with a unique focus on how they interact with underlying system-level features, such as work stealing and memory management. We identify 6 bottlenecks, and found that refactoring them can significantly improve performance and energy efficiency. Our field study includes an in-depth analysis of Akka — a real-world actor framework — and 30 additional open-source ForkJoin projects. We sent our patches to the developers of 15 projects, and 7 out of the 9 projects that replied to our patches have accepted them.
{"title":"Understanding and overcoming parallelism bottlenecks in ForkJoin applications","authors":"G. Pinto, Anthony Canino, F. C. Filho, G. Xu, Yu David Liu","doi":"10.1109/ASE.2017.8115687","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115687","url":null,"abstract":"ForkJoin framework is a widely used parallel programming framework upon which both core concurrency libraries and real-world applications are built. Beneath its simple and user-friendly APIs, ForkJoin is a sophisticated managed parallel runtime unfamiliar to many application programmers: the framework core is a work-stealing scheduler, handles fine-grained tasks, and sustains the pressure from automatic memory management. ForkJoin poses a unique gap in the compute stack between high-level software engineering and low-level system optimization. Understanding and bridging this gap is crucial for the future of parallelism support in JVM-supported applications. This paper describes a comprehensive study on parallelism bottlenecks in ForkJoin applications, with a unique focus on how they interact with underlying system-level features, such as work stealing and memory management. We identify 6 bottlenecks, and found that refactoring them can significantly improve performance and energy efficiency. Our field study includes an in-depth analysis of Akka — a real-world actor framework — and 30 additional open-source ForkJoin projects. We sent our patches to the developers of 15 projects, and 7 out of the 9 projects that replied to our patches have accepted them.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"21 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":"125678968","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.2017.8115697
Satia Herfert, Jibesh Patra, Michael Pradel
Reducing the test input given to a program while preserving some property of interest is important, e.g., to localize faults or to reduce test suites. The well-known delta debugging algorithm and its derivatives automate this task by repeatedly reducing a given input. Unfortunately, these approaches are limited to blindly removing parts of the input and cannot reduce the input by restructuring it. This paper presents the Generalized Tree Reduction (GTR) algorithm, an effective and efficient technique to reduce arbitrary test inputs that can be represented as a tree, such as program code, PDF files, and XML documents. The algorithm combines tree transformations with delta debugging and a greedy backtracking algorithm. To reduce the size of the considered search space, the approach automatically specializes the tree transformations applied by the algorithm based on examples of input trees. We evaluate GTR by reducing Python files that cause interpreter crashes, JavaScript files that cause browser inconsistencies, PDF documents with malicious content, and XML files used to tests an XML validator. The GTR algorithm reduces the trees of these files to 45.3%, 3.6%, 44.2%, and 1.3% of the original size, respectively, outperforming both delta debugging and another state-of-the-art algorithm.
{"title":"Automatically reducing tree-structured test inputs","authors":"Satia Herfert, Jibesh Patra, Michael Pradel","doi":"10.1109/ASE.2017.8115697","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115697","url":null,"abstract":"Reducing the test input given to a program while preserving some property of interest is important, e.g., to localize faults or to reduce test suites. The well-known delta debugging algorithm and its derivatives automate this task by repeatedly reducing a given input. Unfortunately, these approaches are limited to blindly removing parts of the input and cannot reduce the input by restructuring it. This paper presents the Generalized Tree Reduction (GTR) algorithm, an effective and efficient technique to reduce arbitrary test inputs that can be represented as a tree, such as program code, PDF files, and XML documents. The algorithm combines tree transformations with delta debugging and a greedy backtracking algorithm. To reduce the size of the considered search space, the approach automatically specializes the tree transformations applied by the algorithm based on examples of input trees. We evaluate GTR by reducing Python files that cause interpreter crashes, JavaScript files that cause browser inconsistencies, PDF documents with malicious content, and XML files used to tests an XML validator. The GTR algorithm reduces the trees of these files to 45.3%, 3.6%, 44.2%, and 1.3% of the original size, respectively, outperforming both delta debugging and another state-of-the-art algorithm.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"13 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":"124032443","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.2017.8115625
Zeqi Lin, Yanzhen Zou, Junfeng Zhao, Bing Xie
A large software project usually has lots of various textual learning resources about its API, such as tutorials, mailing lists, user forums, etc. Text retrieval technology allows developers to search these API learning resources for related documents using free-text queries, but it suffers from the lexical gap between search queries and documents. In this paper, we propose a novel approach for improving the retrieval of API learning resources through leveraging software-specific conceptual knowledge in software source code. The basic idea behind this approach is that the semantic relatedness between queries and documents could be measured according to software-specific concepts involved in them, and software source code contains a large amount of software-specific conceptual knowledge. In detail, firstly we extract an API graph from software source code and use it as software-specific conceptual knowledge. Then we discover API entities involved in queries and documents, and infer semantic document relatedness through analyzing structural relationships between these API entities. We evaluate our approach in three popular open source software projects. Comparing to the state-of-the-art text retrieval approaches, our approach lead to at least 13.77% improvement with respect to mean average precision (MAP).
{"title":"Improving software text retrieval using conceptual knowledge in source code","authors":"Zeqi Lin, Yanzhen Zou, Junfeng Zhao, Bing Xie","doi":"10.1109/ASE.2017.8115625","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115625","url":null,"abstract":"A large software project usually has lots of various textual learning resources about its API, such as tutorials, mailing lists, user forums, etc. Text retrieval technology allows developers to search these API learning resources for related documents using free-text queries, but it suffers from the lexical gap between search queries and documents. In this paper, we propose a novel approach for improving the retrieval of API learning resources through leveraging software-specific conceptual knowledge in software source code. The basic idea behind this approach is that the semantic relatedness between queries and documents could be measured according to software-specific concepts involved in them, and software source code contains a large amount of software-specific conceptual knowledge. In detail, firstly we extract an API graph from software source code and use it as software-specific conceptual knowledge. Then we discover API entities involved in queries and documents, and infer semantic document relatedness through analyzing structural relationships between these API entities. We evaluate our approach in three popular open source software projects. Comparing to the state-of-the-art text retrieval approaches, our approach lead to at least 13.77% improvement with respect to mean average precision (MAP).","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"23 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":"126602729","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.2017.8115684
Jinpeng Zhou, Sam Silvestro, Hongyu Liu, Yan Cai, Tongping Liu
Deadlocks are critical problems afflicting parallel applications, causing software to hang with no further progress. Existing detection tools suffer not only from significant recording performance overhead, but also from excessive memory and/or storage overhead. In addition, they may generate numerous false alarms. Subsequently, after problems have been reported, tremendous manual effort is required to confirm and fix these deadlocks. This paper designs a novel system, UnDead, that helps defeat deadlocks in production software. Different from existing detection tools, UnDead imposes negligible runtime performance overhead (less than 3 % on average) and small memory overhead (around 6%), without any storage consumption. After detection, UnDead automatically strengthens erroneous programs to prevent future occurrences of both existing and potential deadlocks, which is similar to the existing work—Dimmunix. However, UnDead exceeds Dimmunix with several orders of magnitude lower performance overhead, while eliminating numerous false positives. Extremely low runtime and memory overhead, convenience, and automatic prevention make UnDead an always-on detection tool, and a "band-aid" prevention system for production software.
{"title":"UNDEAD: Detecting and preventing deadlocks in production software","authors":"Jinpeng Zhou, Sam Silvestro, Hongyu Liu, Yan Cai, Tongping Liu","doi":"10.1109/ASE.2017.8115684","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115684","url":null,"abstract":"Deadlocks are critical problems afflicting parallel applications, causing software to hang with no further progress. Existing detection tools suffer not only from significant recording performance overhead, but also from excessive memory and/or storage overhead. In addition, they may generate numerous false alarms. Subsequently, after problems have been reported, tremendous manual effort is required to confirm and fix these deadlocks. This paper designs a novel system, UnDead, that helps defeat deadlocks in production software. Different from existing detection tools, UnDead imposes negligible runtime performance overhead (less than 3 % on average) and small memory overhead (around 6%), without any storage consumption. After detection, UnDead automatically strengthens erroneous programs to prevent future occurrences of both existing and potential deadlocks, which is similar to the existing work—Dimmunix. However, UnDead exceeds Dimmunix with several orders of magnitude lower performance overhead, while eliminating numerous false positives. Extremely low runtime and memory overhead, convenience, and automatic prevention make UnDead an always-on detection tool, and a \"band-aid\" prevention system for production software.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"5 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":"127973459","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.2017.8115707
Stefan Krüger, Sarah Nadi, Michael Reif, Karim Ali, M. Mezini, E. Bodden, Florian Göpfert, Felix Günther, Christian Weinert, Daniel Demmler, Ram Kamath
Previous research suggests that developers often struggle using low-level cryptographic APIs and, as a result, produce insecure code. When asked, developers desire, among other things, more tool support to help them use such APIs. In this paper, we present CogniCrypt, a tool that supports developers with the use of cryptographic APIs. CogniCrypt assists the developer in two ways. First, for a number of common cryptographic tasks, CogniCrypt generates code that implements the respective task in a secure manner. Currently, CogniCrypt supports tasks such as data encryption, communication over secure channels, and long-term archiving. Second, CogniCrypt continuously runs static analyses in the background to ensure a secure integration of the generated code into the developer's workspace. This video demo showcases the main features of CogniCrypt: youtube.com/watch?v=JUq5mRHfAWY.
{"title":"CogniCrypt: Supporting developers in using cryptography","authors":"Stefan Krüger, Sarah Nadi, Michael Reif, Karim Ali, M. Mezini, E. Bodden, Florian Göpfert, Felix Günther, Christian Weinert, Daniel Demmler, Ram Kamath","doi":"10.1109/ASE.2017.8115707","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115707","url":null,"abstract":"Previous research suggests that developers often struggle using low-level cryptographic APIs and, as a result, produce insecure code. When asked, developers desire, among other things, more tool support to help them use such APIs. In this paper, we present CogniCrypt, a tool that supports developers with the use of cryptographic APIs. CogniCrypt assists the developer in two ways. First, for a number of common cryptographic tasks, CogniCrypt generates code that implements the respective task in a secure manner. Currently, CogniCrypt supports tasks such as data encryption, communication over secure channels, and long-term archiving. Second, CogniCrypt continuously runs static analyses in the background to ensure a secure integration of the generated code into the developer's workspace. This video demo showcases the main features of CogniCrypt: youtube.com/watch?v=JUq5mRHfAWY.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"53 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":"132271238","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.2017.8115641
Yasmin Rafiq, Luke Dickens, A. Russo, A. Bandara, Mu Yang, Avelie Stuart, M. Levine, G. Çalikli, B. Price, B. Nuseibeh
Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.
{"title":"Learning to share: Engineering adaptive decision-support for online social networks","authors":"Yasmin Rafiq, Luke Dickens, A. Russo, A. Bandara, Mu Yang, Avelie Stuart, M. Levine, G. Çalikli, B. Price, B. Nuseibeh","doi":"10.1109/ASE.2017.8115641","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115641","url":null,"abstract":"Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"16 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":"134266426","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.2017.8115683
Sara Pérez-Soler, E. Guerra, J. Lara, F. Jurado
We are witnessing a rising role of mobile computing and social networks to perform all sorts of tasks. This way, social networks like Twitter or Telegram are used for leisure, and they frequently serve as a discussion media for work-related activities. In this paper, we propose taking advantage of social networks to enable the collaborative creation of models by groups of users. The process is assisted by modelling bots that orchestrate the collaboration and interpret the users' inputs (in natural language) to incrementally build a (meta-)model. The advantages of this modelling approach include ubiquity of use, automation, assistance, natural user interaction, traceability of design decisions, possibility to incorporate coordination protocols, and seamless integration with the user's normal daily usage of social networks. We present a prototype implementation called SOCIO, able to work over several social networks like Twitter and Telegram, and a preliminary evaluation showing promising results.
{"title":"The rise of the (modelling) bots: Towards assisted modelling via social networks","authors":"Sara Pérez-Soler, E. Guerra, J. Lara, F. Jurado","doi":"10.1109/ASE.2017.8115683","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115683","url":null,"abstract":"We are witnessing a rising role of mobile computing and social networks to perform all sorts of tasks. This way, social networks like Twitter or Telegram are used for leisure, and they frequently serve as a discussion media for work-related activities. In this paper, we propose taking advantage of social networks to enable the collaborative creation of models by groups of users. The process is assisted by modelling bots that orchestrate the collaboration and interpret the users' inputs (in natural language) to incrementally build a (meta-)model. The advantages of this modelling approach include ubiquity of use, automation, assistance, natural user interaction, traceability of design decisions, possibility to incorporate coordination protocols, and seamless integration with the user's normal daily usage of social networks. We present a prototype implementation called SOCIO, able to work over several social networks like Twitter and Telegram, and a preliminary evaluation showing promising results.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"9 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":"134416100","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.2017.8115644
M. Fazzini, A. Orso
Testing of Android apps is particularly challenging due to the fragmentation of the Android ecosystem in terms of both devices and operating system versions. Developers must in fact ensure not only that their apps behave as expected, but also that the apps' behavior is consistent across platforms. To support this task, we propose DiffDroid, a new technique that helps developers automatically find cross-platform inconsistencies (CPIs) in mobile apps. DiffDroid combines input generation and differential testing to compare the behavior of an app on different platforms and identify possible inconsistencies. Given an app, DiffDroid (1) generates test inputs for the app, (2) runs the app with these inputs on a reference device and builds a model of the app behavior, (3) runs the app with the same inputs on a set of other devices, and (4) compares the behavior of the app on these different devices with the model of its behavior on the reference device. We implemented DiFFDRoiD and performed an evaluation of our approach on 5 benchmarks and over 130 platforms. our results show that DiFFDRoiD can identify CPis on real apps efficiently and with a limited number of false positives. DiFFDRoiD and our experimental infrastructure are publicly available.
{"title":"Automated cross-platform inconsistency detection for mobile apps","authors":"M. Fazzini, A. Orso","doi":"10.1109/ASE.2017.8115644","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115644","url":null,"abstract":"Testing of Android apps is particularly challenging due to the fragmentation of the Android ecosystem in terms of both devices and operating system versions. Developers must in fact ensure not only that their apps behave as expected, but also that the apps' behavior is consistent across platforms. To support this task, we propose DiffDroid, a new technique that helps developers automatically find cross-platform inconsistencies (CPIs) in mobile apps. DiffDroid combines input generation and differential testing to compare the behavior of an app on different platforms and identify possible inconsistencies. Given an app, DiffDroid (1) generates test inputs for the app, (2) runs the app with these inputs on a reference device and builds a model of the app behavior, (3) runs the app with the same inputs on a set of other devices, and (4) compares the behavior of the app on these different devices with the model of its behavior on the reference device. We implemented DiFFDRoiD and performed an evaluation of our approach on 5 benchmarks and over 130 platforms. our results show that DiFFDRoiD can identify CPis on real apps efficiently and with a limited number of false positives. DiFFDRoiD and our experimental infrastructure are publicly available.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"59 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":"134560720","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.2017.8115614
Ke Mao, M. Harman, Yue Jia
We show that information extracted from crowd-based testing can enhance automated mobile testing. We introduce Polariz, which generates replicable test scripts from crowd-based testing, extracting cross-app ‘motif’ events: automatically-inferred reusable higher-level event sequences composed of lower-level observed event actions. Our empirical study used 434 crowd workers from Mechanical Turk to perform 1,350 testing tasks on 9 popular Google Play apps, each with at least 1 million user installs. The findings reveal that the crowd was able to achieve 60.5% unique activity coverage and proved to be complementary to automated search-based testing in 5 out of the 9 subjects studied. Our leave-one-out evaluation demonstrates that coverage attainment can be improved (6 out of 9 cases, with no disimprovement on the remaining 3) by combining crowd-based and search-based testing.
{"title":"Crowd intelligence enhances automated mobile testing","authors":"Ke Mao, M. Harman, Yue Jia","doi":"10.1109/ASE.2017.8115614","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115614","url":null,"abstract":"We show that information extracted from crowd-based testing can enhance automated mobile testing. We introduce Polariz, which generates replicable test scripts from crowd-based testing, extracting cross-app ‘motif’ events: automatically-inferred reusable higher-level event sequences composed of lower-level observed event actions. Our empirical study used 434 crowd workers from Mechanical Turk to perform 1,350 testing tasks on 9 popular Google Play apps, each with at least 1 million user installs. The findings reveal that the crowd was able to achieve 60.5% unique activity coverage and proved to be complementary to automated search-based testing in 5 out of the 9 subjects studied. Our leave-one-out evaluation demonstrates that coverage attainment can be improved (6 out of 9 cases, with no disimprovement on the remaining 3) by combining crowd-based and search-based testing.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"11 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":"127806714","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.2017.8115653
Carl Chapman, Peipei Wang, Kathryn T. Stolee
The regular expression (regex) is a powerful tool employed in a large variety of software engineering tasks. However, prior work has shown that regexes can be very complex and that it could be difficult for developers to compose and understand them. This work seeks to identify code smells that impact comprehension. We conduct an empirical study on 42 pairs of behaviorally equivalent but syntactically different regexes using 180 participants and evaluate the understandability of various regex language features. We further analyze regexes in GitHub to find the community standards or the common usages of various features. We found that some regex expression representations are more understandable than others. For example, using a range (e.g., [0–9]) is often more understandable than a default character class (e.g., [d]). We also found that the DFA size of a regex significantly affects comprehension for the regexes studied. The larger the DFA of a regex (up to size eight), the more understandable it was. Finally, we identify smelly and non-smelly regex representations based on a combination of community standards and understandability metrics.
{"title":"Exploring regular expression comprehension","authors":"Carl Chapman, Peipei Wang, Kathryn T. Stolee","doi":"10.1109/ASE.2017.8115653","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115653","url":null,"abstract":"The regular expression (regex) is a powerful tool employed in a large variety of software engineering tasks. However, prior work has shown that regexes can be very complex and that it could be difficult for developers to compose and understand them. This work seeks to identify code smells that impact comprehension. We conduct an empirical study on 42 pairs of behaviorally equivalent but syntactically different regexes using 180 participants and evaluate the understandability of various regex language features. We further analyze regexes in GitHub to find the community standards or the common usages of various features. We found that some regex expression representations are more understandable than others. For example, using a range (e.g., [0–9]) is often more understandable than a default character class (e.g., [d]). We also found that the DFA size of a regex significantly affects comprehension for the regexes studied. The larger the DFA of a regex (up to size eight), the more understandable it was. Finally, we identify smelly and non-smelly regex representations based on a combination of community standards and understandability metrics.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"108 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":"127951012","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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