Jiwei Yan, Linjie Pan, Yaqi Li, Jun Yan, Jian Zhang
Model-based GUI exploration techniques are widely used to generate test cases for event-driven programs (such as Android apps). These techniques traverse the elements of screens during the user interaction and simultaneously construct the GUI model. Although there are a number of automatic model-based exploration tools, most of them pay more attention to the exploration procedure than the model reusing. This paper presents LAND, an effective and user-friendly test generation tool based on GUI exploration of Android apps, which constructs an elaborate window transition model ``LATTE'' that considers more Android specific characteristics and provides a customizable test generation interface by reusing the model. Experiments on 20 real-world Android apps are conducted to construct their models as well as test cases. The experimental results indicate that LAND can achieve higher code coverage and trigger exceptions in shorter sequence. It is also demonstrated that LATTE can be well reused under different requirements of test suite generation. A demo video of our tool can be found at the website https://www.youtube.com/watch?v=iqtr12eiJ_0.
{"title":"LAND: a user-friendly and customizable test generation tool for Android apps","authors":"Jiwei Yan, Linjie Pan, Yaqi Li, Jun Yan, Jian Zhang","doi":"10.1145/3213846.3229500","DOIUrl":"https://doi.org/10.1145/3213846.3229500","url":null,"abstract":"Model-based GUI exploration techniques are widely used to generate test cases for event-driven programs (such as Android apps). These techniques traverse the elements of screens during the user interaction and simultaneously construct the GUI model. Although there are a number of automatic model-based exploration tools, most of them pay more attention to the exploration procedure than the model reusing. This paper presents LAND, an effective and user-friendly test generation tool based on GUI exploration of Android apps, which constructs an elaborate window transition model ``LATTE'' that considers more Android specific characteristics and provides a customizable test generation interface by reusing the model. Experiments on 20 real-world Android apps are conducted to construct their models as well as test cases. The experimental results indicate that LAND can achieve higher code coverage and trigger exceptions in shorter sequence. It is also demonstrated that LATTE can be well reused under different requirements of test suite generation. A demo video of our tool can be found at the website https://www.youtube.com/watch?v=iqtr12eiJ_0.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73091393","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}
Developers strive to build feature-filled apps that are responsive and consume as few resources as possible. Most of these apps make use of local databases to store and access data locally. Prior work has found that local database services have become one of the major drivers of a mobile device's resource consumption. In this paper we propose an approach to reduce the energy consumption and improve runtime performance of database operations in Android apps by optimizing inefficient database writes. Our approach automatically detects database writes that happen within loops and that will trigger inefficient autocommit behaviors. Our approach then uses additional analyses to identify those that are optimizable and rewrites the code so that it is more efficient. We evaluated our approach on a set of marketplace Android apps and found it could reduce the energy and runtime of events containing the inefficient database writes by 25% to 90% and needed, on average, thirty-six seconds to analyze and transform each app.
{"title":"Remove RATs from your code: automated optimization of resource inefficient database writes for mobile applications","authors":"Yingjun Lyu, Ding Li, William G. J. Halfond","doi":"10.1145/3213846.3213865","DOIUrl":"https://doi.org/10.1145/3213846.3213865","url":null,"abstract":"Developers strive to build feature-filled apps that are responsive and consume as few resources as possible. Most of these apps make use of local databases to store and access data locally. Prior work has found that local database services have become one of the major drivers of a mobile device's resource consumption. In this paper we propose an approach to reduce the energy consumption and improve runtime performance of database operations in Android apps by optimizing inefficient database writes. Our approach automatically detects database writes that happen within loops and that will trigger inefficient autocommit behaviors. Our approach then uses additional analyses to identify those that are optimizable and rewrites the code so that it is more efficient. We evaluated our approach on a set of marketplace Android apps and found it could reduce the energy and runtime of events containing the inefficient database writes by 25% to 90% and needed, on average, thirty-six seconds to analyze and transform each app.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84193947","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}
M. Fazzini, Martin Prammer, Marcelo d’Amorim, A. Orso
When users experience a software failure, they have the option of submitting a bug report and provide information about the failure and how it happened. If the bug report contains enough information, developers can then try to recreate the issue and investigate it, so as to eliminate its causes. Unfortunately, the number of bug reports filed by users is typically large, and the tasks of analyzing bug reports and reproducing the issues described therein can be extremely time consuming. To help make this process more efficient, in this paper we propose Yakusu, a technique that uses a combination of program analysis and natural language processing techniques to generate executable test cases from bug reports. We implemented Yakusu for Android apps and performed an empirical evaluation on a set of over 60 real bug reports for different real-world apps. Overall, our technique was successful in 59.7% of the cases; that is, for a majority of the bug reports, developers would not have to study the report to reproduce the issue described and could simply use the test cases automatically generated by Yakusu. Furthermore, in many of the remaining cases, Yakusu was unsuccessful due to limitations that can be addressed in future work.
{"title":"Automatically translating bug reports into test cases for mobile apps","authors":"M. Fazzini, Martin Prammer, Marcelo d’Amorim, A. Orso","doi":"10.1145/3213846.3213869","DOIUrl":"https://doi.org/10.1145/3213846.3213869","url":null,"abstract":"When users experience a software failure, they have the option of submitting a bug report and provide information about the failure and how it happened. If the bug report contains enough information, developers can then try to recreate the issue and investigate it, so as to eliminate its causes. Unfortunately, the number of bug reports filed by users is typically large, and the tasks of analyzing bug reports and reproducing the issues described therein can be extremely time consuming. To help make this process more efficient, in this paper we propose Yakusu, a technique that uses a combination of program analysis and natural language processing techniques to generate executable test cases from bug reports. We implemented Yakusu for Android apps and performed an empirical evaluation on a set of over 60 real bug reports for different real-world apps. Overall, our technique was successful in 59.7% of the cases; that is, for a majority of the bug reports, developers would not have to study the report to reproduce the issue described and could simply use the test cases automatically generated by Yakusu. Furthermore, in many of the remaining cases, Yakusu was unsuccessful due to limitations that can be addressed in future work.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76459010","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}
{"title":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","authors":"","doi":"10.1145/3213846","DOIUrl":"https://doi.org/10.1145/3213846","url":null,"abstract":"","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86784045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, there has been a growing interest in making education widely accessible using Internet technologies. Whether it is Massive Open Online Courses (MOOCs) or simply college courses offered to a large student population using an online platform, both education-focused companies and universities, often in collaboration with one another, have been investing massively in online education. The fact that hundreds, and more often thousands, of students take these online courses raises scalability challenges in assessing student assignments. In this paper, in particular, we present a technique (GUITestMigrator) that addresses the challenge of assessing mobile app coding assignments. Given a set of apps that implement the same specification, but can have completely different user interfaces, instructors normally have to manually run and check each app to make sure it behaves correctly and according to the specification. GUITestMigrator, conversely, allows for developing tests for one of these apps and automatically migrating these tests to the other apps, thus dramatically reducing the burden on the instructor. We implemented GUITestMigrator for Android apps and evaluated it on three sets of apps developed over three different semesters by students of an online graduate-level software engineering course. Our initial results show that our approach is promising and motivates further research in this direction. The paper also discusses possible applications of this approach for test evolution and test migration for real-world apps.
{"title":"Test migration for efficient large-scale assessment of mobile app coding assignments","authors":"Farnaz Behrang, A. Orso","doi":"10.1145/3213846.3213854","DOIUrl":"https://doi.org/10.1145/3213846.3213854","url":null,"abstract":"In recent years, there has been a growing interest in making education widely accessible using Internet technologies. Whether it is Massive Open Online Courses (MOOCs) or simply college courses offered to a large student population using an online platform, both education-focused companies and universities, often in collaboration with one another, have been investing massively in online education. The fact that hundreds, and more often thousands, of students take these online courses raises scalability challenges in assessing student assignments. In this paper, in particular, we present a technique (GUITestMigrator) that addresses the challenge of assessing mobile app coding assignments. Given a set of apps that implement the same specification, but can have completely different user interfaces, instructors normally have to manually run and check each app to make sure it behaves correctly and according to the specification. GUITestMigrator, conversely, allows for developing tests for one of these apps and automatically migrating these tests to the other apps, thus dramatically reducing the burden on the instructor. We implemented GUITestMigrator for Android apps and evaluated it on three sets of apps developed over three different semesters by students of an online graduate-level software engineering course. Our initial results show that our approach is promising and motivates further research in this direction. The paper also discusses possible applications of this approach for test evolution and test migration for real-world apps.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79623983","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}
The dynamic proxy API is one of Java’s most widely-used dynamic features, permitting principled run-time code generation and link- ing. Dynamic proxies can implement any set of interfaces and for- ward method calls to a special object that handles them reflectively. The flexibility of dynamic proxies, however, comes at the cost of having a dynamically generated layer of bytecode that cannot be penetrated by current static analyses. In this paper, we observe that the dynamic proxy API is stylized enough to permit static analysis. We show how the semantics of dynamic proxies can be modeled in a straightforward manner as logical rules in the Doop static analysis framework. This concise set of rules enables Doop’s standard analyses to process code behind dynamic proxies. We evaluate our approach by analyzing XCorpus, a corpus of real-world Java programs: we fully handle 95% of its reported proxy creation sites. Our handling results in the analysis of significant portions of previously unreachable or incompletely- modeled code.
{"title":"Static analysis of Java dynamic proxies","authors":"G. Fourtounis, George Kastrinis, Y. Smaragdakis","doi":"10.1145/3213846.3213864","DOIUrl":"https://doi.org/10.1145/3213846.3213864","url":null,"abstract":"The dynamic proxy API is one of Java’s most widely-used dynamic features, permitting principled run-time code generation and link- ing. Dynamic proxies can implement any set of interfaces and for- ward method calls to a special object that handles them reflectively. The flexibility of dynamic proxies, however, comes at the cost of having a dynamically generated layer of bytecode that cannot be penetrated by current static analyses. In this paper, we observe that the dynamic proxy API is stylized enough to permit static analysis. We show how the semantics of dynamic proxies can be modeled in a straightforward manner as logical rules in the Doop static analysis framework. This concise set of rules enables Doop’s standard analyses to process code behind dynamic proxies. We evaluate our approach by analyzing XCorpus, a corpus of real-world Java programs: we fully handle 95% of its reported proxy creation sites. Our handling results in the analysis of significant portions of previously unreachable or incompletely- modeled code.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90576709","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}
Jian Gao, Xin Yang, Yu Jiang, Han Liu, Weiliang Ying, Wanting Sun, M. Gu
As a result of the increasing number of concurrent programs, the researchers put forward a number of tools with different implementation strategies to detect data race. However, confirming data races from the collection of true and false positives reported by race detectors is extremely the time-consuming process during the evaluation period. In this paper, we presented ComRaDe, a management platform for concurrent testing of data race with three main functions: manage and filter data races, run evaluation programs to select race detectors, generate detection report automatically. We integrated and compared three different race detectors on ComRaDe in terms of race detection capability. The results demonstrated the potential of ComRaDe on effectively identifying the advantages and limitations of different race detectors, and in further helping researchers to select and improve the capability of detectors for its convenience.
{"title":"Managing concurrent testing of data race with ComRaDe","authors":"Jian Gao, Xin Yang, Yu Jiang, Han Liu, Weiliang Ying, Wanting Sun, M. Gu","doi":"10.1145/3213846.3229502","DOIUrl":"https://doi.org/10.1145/3213846.3229502","url":null,"abstract":"As a result of the increasing number of concurrent programs, the researchers put forward a number of tools with different implementation strategies to detect data race. However, confirming data races from the collection of true and false positives reported by race detectors is extremely the time-consuming process during the evaluation period. In this paper, we presented ComRaDe, a management platform for concurrent testing of data race with three main functions: manage and filter data races, run evaluation programs to select race detectors, generate detection report automatically. We integrated and compared three different race detectors on ComRaDe in terms of race detection capability. The results demonstrated the potential of ComRaDe on effectively identifying the advantages and limitations of different race detectors, and in further helping researchers to select and improve the capability of detectors for its convenience.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89409263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software typically outlives the platform that it was originally written for. To smooth the transition to new tools and platforms, programs should depend on the underlying platform as little as possible. In practice, however, software build processes are highly sensitive to their build platform, notably the implementation of the compiler and standard library. This makes it difficult to port existing, mature software to emerging platforms---web based runtimes like WebAssembly, resource-constrained environments for Internet-of-Things devices, or innovative new operating systems like Fuchsia. We present Tuscan, a framework for conducting automatic, deterministic, reproducible tests on build systems. Tuscan is the first framework to solve the problem of reproducibly testing builds cross-platform at massive scale. We also wrote a build wrapper, Red, which hijacks builds to tolerate common failures that arise from platform dependence, allowing the test harness to discover errors later in the build. Authors of innovative platforms can use Tuscan and Red to test the extent of unportability in the software ecosystem, and to quantify the effort necessary to port legacy software. We evaluated Tuscan by building an operating system distribution, consisting of 2,699 Red-wrapped programs, on four platforms, yielding a `catalog' of the most common portability errors. This catalog informs data-driven porting decisions and motivates changes to programs, build systems, and language standards; systematically quantifies problems that platform writers have hitherto discovered only on an ad-hoc basis; and forms the basis for a common substrate of portability fixes that developers can apply to their software.
{"title":"Making data-driven porting decisions with Tuscan","authors":"Kareem Khazem, Earl T. Barr, Petr Hosek","doi":"10.1145/3213846.3213855","DOIUrl":"https://doi.org/10.1145/3213846.3213855","url":null,"abstract":"Software typically outlives the platform that it was originally written for. To smooth the transition to new tools and platforms, programs should depend on the underlying platform as little as possible. In practice, however, software build processes are highly sensitive to their build platform, notably the implementation of the compiler and standard library. This makes it difficult to port existing, mature software to emerging platforms---web based runtimes like WebAssembly, resource-constrained environments for Internet-of-Things devices, or innovative new operating systems like Fuchsia. We present Tuscan, a framework for conducting automatic, deterministic, reproducible tests on build systems. Tuscan is the first framework to solve the problem of reproducibly testing builds cross-platform at massive scale. We also wrote a build wrapper, Red, which hijacks builds to tolerate common failures that arise from platform dependence, allowing the test harness to discover errors later in the build. Authors of innovative platforms can use Tuscan and Red to test the extent of unportability in the software ecosystem, and to quantify the effort necessary to port legacy software. We evaluated Tuscan by building an operating system distribution, consisting of 2,699 Red-wrapped programs, on four platforms, yielding a `catalog' of the most common portability errors. This catalog informs data-driven porting decisions and motivates changes to programs, build systems, and language standards; systematically quantifies problems that platform writers have hitherto discovered only on an ad-hoc basis; and forms the basis for a common substrate of portability fixes that developers can apply to their software.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79486845","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}
Gunel Jahangirova, D. Clark, M. Harman, P. Tonella
The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. We introduce OASIs, a search-based tool for Java that assists testers in oracle assessment and improvement. It does so by combining test case generation to reveal false positives and mutation testing to reveal false negatives. In this work, we describe how OASIs works, provide details of its implementation, and explain how it can be used in an iterative oracle improvement process with a human in the loop. Finally, we present a summary of previous empirical evaluation showing that the fault detection rate of the oracles after improvement using OASIs increases, on average, by 48.6%.
{"title":"OASIs: oracle assessment and improvement tool","authors":"Gunel Jahangirova, D. Clark, M. Harman, P. Tonella","doi":"10.1145/3213846.3229503","DOIUrl":"https://doi.org/10.1145/3213846.3229503","url":null,"abstract":"The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. We introduce OASIs, a search-based tool for Java that assists testers in oracle assessment and improvement. It does so by combining test case generation to reveal false positives and mutation testing to reveal false negatives. In this work, we describe how OASIs works, provide details of its implementation, and explain how it can be used in an iterative oracle improvement process with a human in the loop. Finally, we present a summary of previous empirical evaluation showing that the fault detection rate of the oracles after improvement using OASIs increases, on average, by 48.6%.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79053965","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}
Floating-point types are notorious for their intricate representation. The effective use of mixed precision, i.e., using various precisions in different computations, is critical to achieve a good balance between accuracy and performance. Unfortunately, reasoning about mixed precision is difficult even for numerical experts. Techniques have been proposed to systematically search over floating-point variables and/or program instructions to find a faster, mixed-precision version of a given program. These techniques, however, are characterized by their black box nature, and face scalability limitations due to the large search space. In this paper, we exploit the community structure of floating-point variables to devise a scalable hierarchical search for precision tuning. Specifically, we perform dependence analysis and edge profiling to create a weighted dependence graph that presents a network of floating-point variables. We then formulate hierarchy construction on the network as a community detection problem, and present a hierarchical search algorithm that iteratively lowers precision with regard to communities. We implement our algorithm in the tool HiFPTuner, and show that it exhibits higher search efficiency over the state of the art for 75.9% of the experiments taking 59.6% less search time on average. Moreover, HiFPTuner finds more profitable configurations for 51.7% of the experiments, with one known to be as good as the global optimum found through exhaustive search.
{"title":"Exploiting community structure for floating-point precision tuning","authors":"Hui Guo, Cindy Rubio-González","doi":"10.1145/3213846.3213862","DOIUrl":"https://doi.org/10.1145/3213846.3213862","url":null,"abstract":"Floating-point types are notorious for their intricate representation. The effective use of mixed precision, i.e., using various precisions in different computations, is critical to achieve a good balance between accuracy and performance. Unfortunately, reasoning about mixed precision is difficult even for numerical experts. Techniques have been proposed to systematically search over floating-point variables and/or program instructions to find a faster, mixed-precision version of a given program. These techniques, however, are characterized by their black box nature, and face scalability limitations due to the large search space. In this paper, we exploit the community structure of floating-point variables to devise a scalable hierarchical search for precision tuning. Specifically, we perform dependence analysis and edge profiling to create a weighted dependence graph that presents a network of floating-point variables. We then formulate hierarchy construction on the network as a community detection problem, and present a hierarchical search algorithm that iteratively lowers precision with regard to communities. We implement our algorithm in the tool HiFPTuner, and show that it exhibits higher search efficiency over the state of the art for 75.9% of the experiments taking 59.6% less search time on average. Moreover, HiFPTuner finds more profitable configurations for 51.7% of the experiments, with one known to be as good as the global optimum found through exhaustive search.","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86157095","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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