Nowadays, Apple iOS and Android are two most popular platforms for mobile applications. To attract more users, many software companies and organizations are migrating their applications from one platform to the other, and besides source files, they also need to migrate their GUI tests. The migration of GUI tests is tedious and difficult to be automated, since two platforms have subtle differences and there are often few or even no migrated GUI tests for learning. To address the problem, in this paper, we propose a novel approach, TestMig, that migrates GUI tests from iOS to Android, without any migrated code samples. Specifically, TestMig first executes the GUI tests of the iOS version, and records their GUI event sequences. Guided by the iOS GUI events, TestMig explores the Android version of the application to generate the corresponding Android event sequences. We conducted an evaluation on five well known mobile applications: 2048, SimpleNote, Wire, Wikipedia, and WordPress. The results show that, on average, TestMig correctly converts 80.2% of recorded iOS UI events to Android UI events and have them successfully executed, and our migrated Android test cases achieve similar statement coverage compared with the original iOS test cases (59.7% vs 60.4%).
如今，苹果iOS和安卓是两个最流行的移动应用平台。为了吸引更多的用户，许多软件公司和组织正在将他们的应用程序从一个平台迁移到另一个平台，除了源文件之外，他们还需要迁移他们的GUI测试。GUI测试的迁移是乏味且难以自动化的，因为两个平台有细微的差异，并且通常很少甚至没有迁移的GUI测试可供学习。为了解决这个问题，在本文中，我们提出了一种新颖的方法，TestMig，它可以将GUI测试从iOS迁移到Android，而不需要迁移任何代码样本。具体来说，TestMig首先执行iOS版本的GUI测试，并记录其GUI事件序列。在iOS GUI事件的指导下，TestMig探索应用程序的Android版本以生成相应的Android事件序列。我们对五个知名的移动应用程序进行了评估:2048、SimpleNote、Wire、Wikipedia和WordPress。结果表明，平均而言，TestMig正确地将80.2%记录的iOS UI事件转换为Android UI事件，并使它们成功执行，我们迁移的Android测试用例与原始iOS测试用例相比实现了相似的语句覆盖率(59.7% vs 60.4%)。
{"title":"TestMig: migrating GUI test cases from iOS to Android","authors":"Xue Qin, Hao Zhong, Xiaoyin Wang","doi":"10.1145/3293882.3330575","DOIUrl":"https://doi.org/10.1145/3293882.3330575","url":null,"abstract":"Nowadays, Apple iOS and Android are two most popular platforms for mobile applications. To attract more users, many software companies and organizations are migrating their applications from one platform to the other, and besides source files, they also need to migrate their GUI tests. The migration of GUI tests is tedious and difficult to be automated, since two platforms have subtle differences and there are often few or even no migrated GUI tests for learning. To address the problem, in this paper, we propose a novel approach, TestMig, that migrates GUI tests from iOS to Android, without any migrated code samples. Specifically, TestMig first executes the GUI tests of the iOS version, and records their GUI event sequences. Guided by the iOS GUI events, TestMig explores the Android version of the application to generate the corresponding Android event sequences. We conducted an evaluation on five well known mobile applications: 2048, SimpleNote, Wire, Wikipedia, and WordPress. The results show that, on average, TestMig correctly converts 80.2% of recorded iOS UI events to Android UI events and have them successfully executed, and our migrated Android test cases achieve similar statement coverage compared with the original iOS test cases (59.7% vs 60.4%).","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86532013","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}
Popular Android applications undergo frequent releases. Ensuring functional testing of the new features, as well as regression testing of the previous functionality, are time-consuming and error-prone. Thus, there is a need for a tool that eases the testing efforts as well as saves the overall time of the product release cycle. In this work, we present QADroid, the first activity- and event-aware regression selection tool for Android apps. Salient features of QADroid are: (i) a richer change-set analyzer that covers code as well as non-code components for regression, (ii) it presents a pictorial representation of the app’s functioning, and (iii) it displays the regression points in the app as a mapping between activities to user-elements to events. Features (ii) and (iii) help the testers in understanding the technical findings better. We evaluated QADroid on 1105 releases of 50 open source Android projects. The results show that QADroid reduced the activity selection by 58% and event selection by 74% compared to the traditional way of exhaustive testing of all activities and events, thereby significantly reducing the manual testing efforts.
{"title":"QADroid: regression event selection for Android applications","authors":"Aman Sharma, R. Nasre","doi":"10.1145/3293882.3330550","DOIUrl":"https://doi.org/10.1145/3293882.3330550","url":null,"abstract":"Popular Android applications undergo frequent releases. Ensuring functional testing of the new features, as well as regression testing of the previous functionality, are time-consuming and error-prone. Thus, there is a need for a tool that eases the testing efforts as well as saves the overall time of the product release cycle. In this work, we present QADroid, the first activity- and event-aware regression selection tool for Android apps. Salient features of QADroid are: (i) a richer change-set analyzer that covers code as well as non-code components for regression, (ii) it presents a pictorial representation of the app’s functioning, and (iii) it displays the regression points in the app as a mapping between activities to user-elements to events. Features (ii) and (iii) help the testers in understanding the technical findings better. We evaluated QADroid on 1105 releases of 50 open source Android projects. The results show that QADroid reduced the activity selection by 58% and event selection by 74% compared to the traditional way of exhaustive testing of all activities and events, thereby significantly reducing the manual testing efforts.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80582231","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}
E. Albert, J. Fernández, Pablo Gordillo, G. Román-Díez, A. Rubio
Ethereum smart contracts are public, immutable and distributed and, as such, they are prone to vulnerabilities sourcing from programming mistakes of developers. This paper presents SAFEVM, a verification tool for Ethereum smart contracts that makes use of state-of-the-art verification engines for C programs. SAFEVM takes as input an Ethereum smart contract (provided either in Solidity source code, or in compiled EVM bytecode), optionally with assert and require verification annotations, and produces in the output a report with the verification results. Besides general safety annotations, SAFEVM handles the verification of array accesses: it automatically generates SV-COMP verification assertions such that C verification engines can prove safety of array accesses. Our experimental evaluation has been undertaken on all contracts pulled from etherscan.io (more than 24,000) by using as back-end verifiers CPAchecker, SeaHorn and VeryMax.
{"title":"SAFEVM: a safety verifier for Ethereum smart contracts","authors":"E. Albert, J. Fernández, Pablo Gordillo, G. Román-Díez, A. Rubio","doi":"10.1145/3293882.3338999","DOIUrl":"https://doi.org/10.1145/3293882.3338999","url":null,"abstract":"Ethereum smart contracts are public, immutable and distributed and, as such, they are prone to vulnerabilities sourcing from programming mistakes of developers. This paper presents SAFEVM, a verification tool for Ethereum smart contracts that makes use of state-of-the-art verification engines for C programs. SAFEVM takes as input an Ethereum smart contract (provided either in Solidity source code, or in compiled EVM bytecode), optionally with assert and require verification annotations, and produces in the output a report with the verification results. Besides general safety annotations, SAFEVM handles the verification of array accesses: it automatically generates SV-COMP verification assertions such that C verification engines can prove safety of array accesses. Our experimental evaluation has been undertaken on all contracts pulled from etherscan.io (more than 24,000) by using as back-end verifiers CPAchecker, SeaHorn and VeryMax.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88837631","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}
Kui Liu, Anil Koyuncu, Dongsun Kim, Tegawendé F. Bissyandé
We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR).
{"title":"TBar: revisiting template-based automated program repair","authors":"Kui Liu, Anil Koyuncu, Dongsun Kim, Tegawendé F. Bissyandé","doi":"10.1145/3293882.3330577","DOIUrl":"https://doi.org/10.1145/3293882.3330577","url":null,"abstract":"We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR).","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"1998 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88207330","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}
Christian Klinger, M. Christakis, Valentin Wüstholz
In the last decades, numerous program analyzers have been developed both in academia and industry. Despite their abundance however, there is currently no systematic way of comparing the effectiveness of different analyzers on arbitrary code. In this paper, we present the first automated technique for differentially testing soundness and precision of program analyzers. We used our technique to compare six mature, state-of-the art analyzers on tens of thousands of automatically generated benchmarks. Our technique detected soundness and precision issues in most analyzers, and we evaluated the implications of these issues to both designers and users of program analyzers.
{"title":"Differentially testing soundness and precision of program analyzers","authors":"Christian Klinger, M. Christakis, Valentin Wüstholz","doi":"10.1145/3293882.3330553","DOIUrl":"https://doi.org/10.1145/3293882.3330553","url":null,"abstract":"In the last decades, numerous program analyzers have been developed both in academia and industry. Despite their abundance however, there is currently no systematic way of comparing the effectiveness of different analyzers on arbitrary code. In this paper, we present the first automated technique for differentially testing soundness and precision of program analyzers. We used our technique to compare six mature, state-of-the art analyzers on tens of thousands of automatically generated benchmarks. Our technique detected soundness and precision issues in most analyzers, and we evaluated the implications of these issues to both designers and users of program analyzers.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"262 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91326708","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}
Rohan Padhye, Caroline Lemieux, Koushik Sen, Mike Papadakis, Yves Le Traon
Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed input and executes the core logic of the program. Generator-based testing tools in the lineage of QuickCheck are a promising way to generate random syntactically valid test inputs for these programs. We present Zest, a technique which automatically guides QuickCheck-like random input generators to better explore the semantic analysis stage of test programs. Zest converts random-input generators into deterministic parametric input generators. We present the key insight that mutations in the untyped parameter domain map to structural mutations in the input domain. Zest leverages program feedback in the form of code coverage and input validity to perform feedback-directed parameter search. We evaluate Zest against AFL and QuickCheck on five Java programs: Maven, Ant, BCEL, Closure, and Rhino. Zest covers 1.03x-2.81x as many branches within the benchmarks' semantic analysis stages as baseline techniques. Further, we find 10 new bugs in the semantic analysis stages of these benchmarks. Zest is the most effective technique in finding these bugs reliably and quickly, requiring at most 10 minutes on average to find each bug.
{"title":"Semantic fuzzing with zest","authors":"Rohan Padhye, Caroline Lemieux, Koushik Sen, Mike Papadakis, Yves Le Traon","doi":"10.1145/3293882.3330576","DOIUrl":"https://doi.org/10.1145/3293882.3330576","url":null,"abstract":"Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed input and executes the core logic of the program. Generator-based testing tools in the lineage of QuickCheck are a promising way to generate random syntactically valid test inputs for these programs. We present Zest, a technique which automatically guides QuickCheck-like random input generators to better explore the semantic analysis stage of test programs. Zest converts random-input generators into deterministic parametric input generators. We present the key insight that mutations in the untyped parameter domain map to structural mutations in the input domain. Zest leverages program feedback in the form of code coverage and input validity to perform feedback-directed parameter search. We evaluate Zest against AFL and QuickCheck on five Java programs: Maven, Ant, BCEL, Closure, and Rhino. Zest covers 1.03x-2.81x as many branches within the benchmarks' semantic analysis stages as baseline techniques. Further, we find 10 new bugs in the semantic analysis stages of these benchmarks. Zest is the most effective technique in finding these bugs reliably and quickly, requiring at most 10 minutes on average to find each bug.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81095163","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}
Aashish Kolluri, I. Nikolic, Ilya Sergey, Aquinas Hobor, P. Saxena
We investigate a family of bugs in blockchain-based smart contracts, which we dub event-ordering (or EO) bugs. These bugs are intimately related to the dynamic ordering of contract events, i.e. calls of its functions, and enable potential exploits of millions of USD worth of crypto-coins. Previous techniques to detect EO bugs have been restricted to those bugs that involve just one or two event orderings. Our work provides a new formulation of the general class of EO bugs arising in long permutations of such events by using techniques from concurrent program analysis. The technical challenge in detecting EO bugs in blockchain contracts is the inherent combinatorial blowup in path and state space analysis, even for simple contracts. We propose the first use of partial-order reduction techniques, using automatically extracted happens-before relations along with several dynamic symbolic execution optimizations. We build EthRacer, an automatic analysis tool that runs directly on Ethereum bytecode and requires no hints from users. It flags 8% of over 10, 000 contracts analyzed, providing compact event traces (witnesses) that human analysts can examine in only a few minutes per contract. More than half of the flagged contracts are likely to have unintended behaviour.
{"title":"Exploiting the laws of order in smart contracts","authors":"Aashish Kolluri, I. Nikolic, Ilya Sergey, Aquinas Hobor, P. Saxena","doi":"10.1145/3293882.3330560","DOIUrl":"https://doi.org/10.1145/3293882.3330560","url":null,"abstract":"We investigate a family of bugs in blockchain-based smart contracts, which we dub event-ordering (or EO) bugs. These bugs are intimately related to the dynamic ordering of contract events, i.e. calls of its functions, and enable potential exploits of millions of USD worth of crypto-coins. Previous techniques to detect EO bugs have been restricted to those bugs that involve just one or two event orderings. Our work provides a new formulation of the general class of EO bugs arising in long permutations of such events by using techniques from concurrent program analysis. The technical challenge in detecting EO bugs in blockchain contracts is the inherent combinatorial blowup in path and state space analysis, even for simple contracts. We propose the first use of partial-order reduction techniques, using automatically extracted happens-before relations along with several dynamic symbolic execution optimizations. We build EthRacer, an automatic analysis tool that runs directly on Ethereum bytecode and requires no hints from users. It flags 8% of over 10, 000 contracts analyzed, providing compact event traces (witnesses) that human analysts can examine in only a few minutes per contract. More than half of the flagged contracts are likely to have unintended behaviour.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83801358","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}
Automated Program Repair (APR) is one of the most recent advances in automated debugging, and can directly fix buggy programs with minimal human intervention. Although various advanced APR techniques (including search-based or semantic-based ones) have been proposed, they mainly work at the source-code level and it is not clear how bytecode-level APR performs in practice. Also, empirical studies of the existing techniques on bugs beyond what has been reported in the original papers are rather limited. In this paper, we implement the first practical bytecode-level APR technique, PraPR, and present the first extensive study on fixing real-world bugs (e.g., Defects4J bugs) using JVM bytecode mutation. The experimental results show that surprisingly even PraPR with only the basic traditional mutators can produce genuine fixes for 17 bugs; with simple additional commonly used APR mutators, PraPR is able to produce genuine fixes for 43 bugs, significantly outperforming state-of-the-art APR, while being over 10X faster. Furthermore, we performed an extensive study of PraPR and other recent APR tools on a large number of additional real-world bugs, and demonstrated the overfitting problem of recent advanced APR tools for the first time. Lastly, PraPR has also successfully fixed bugs for other JVM languages (e.g., for the popular Kotlin language), indicating PraPR can greatly complement existing source-code-level APR.
{"title":"Practical program repair via bytecode mutation","authors":"Ali Ghanbari, Lingming Zhang","doi":"10.1145/3293882.3330559","DOIUrl":"https://doi.org/10.1145/3293882.3330559","url":null,"abstract":"Automated Program Repair (APR) is one of the most recent advances in automated debugging, and can directly fix buggy programs with minimal human intervention. Although various advanced APR techniques (including search-based or semantic-based ones) have been proposed, they mainly work at the source-code level and it is not clear how bytecode-level APR performs in practice. Also, empirical studies of the existing techniques on bugs beyond what has been reported in the original papers are rather limited. In this paper, we implement the first practical bytecode-level APR technique, PraPR, and present the first extensive study on fixing real-world bugs (e.g., Defects4J bugs) using JVM bytecode mutation. The experimental results show that surprisingly even PraPR with only the basic traditional mutators can produce genuine fixes for 17 bugs; with simple additional commonly used APR mutators, PraPR is able to produce genuine fixes for 43 bugs, significantly outperforming state-of-the-art APR, while being over 10X faster. Furthermore, we performed an extensive study of PraPR and other recent APR tools on a large number of additional real-world bugs, and demonstrated the overfitting problem of recent advanced APR tools for the first time. Lastly, PraPR has also successfully fixed bugs for other JVM languages (e.g., for the popular Kotlin language), indicating PraPR can greatly complement existing source-code-level APR.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78646411","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}
Testing user interfaces (UIs) is a challenging task. Ideally, every sequence of UI elements should be tested to guarantee that the application works correctly. This is, however, unfeasible due to the number of UI elements in an application. A better approach is to limit the evaluation to UI elements that affect a specific functionality. In this paper I present a novel technique to identify the relation between UI elements using the statically extracted data flows. I also present a method to refine these relations using dynamic analysis, in order to ensure that relations extracted from unreachable data flows are removed. Using these relations it is possible to more efficiently test a functionality. Finally, I present an approach to evaluate how these UI-aware data flows can be used as an heuristic to measure test coverage.
{"title":"Data flow oriented UI testing: exploiting data flows and UI elements to test Android applications","authors":"N. P. Borges","doi":"10.1145/3092703.3098234","DOIUrl":"https://doi.org/10.1145/3092703.3098234","url":null,"abstract":"Testing user interfaces (UIs) is a challenging task. Ideally, every sequence of UI elements should be tested to guarantee that the application works correctly. This is, however, unfeasible due to the number of UI elements in an application. A better approach is to limit the evaluation to UI elements that affect a specific functionality. In this paper I present a novel technique to identify the relation between UI elements using the statically extracted data flows. I also present a method to refine these relations using dynamic analysis, in order to ensure that relations extracted from unreachable data flows are removed. Using these relations it is possible to more efficiently test a functionality. Finally, I present an approach to evaluate how these UI-aware data flows can be used as an heuristic to measure test coverage.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"42 1","pages":"432-435"},"PeriodicalIF":0.0,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90393894","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 number of smartphones shipped in 2014 will be four times larger than the number of PCs. Compared to PCs, smartphones have limited computing resources, and smartphone applications are more prone to performance problems. Traditionally, developers use profilers to detect performance problems by running applications with relatively large inputs. Unfortunately, for smartphone applications, the developer cannot easily control the input, because smartphone applications interact heavily with the environment. Given a run on a small input, how can a developer detect performance problems that would occur for a run with large input? We present SUNCAT, a novel technique that helps developers understand and predict performance problems in smartphone applications. The developer runs the application using a common input, typically small, and SUNCAT presents a prioritized list of repetition patterns that summarize the current run plus additional information to help the developer understand how these patterns may grow in the future runs with large inputs. We implemented SUNCAT for Windows Phone systems and used it to understand the performance characteristics of 29 usage scenarios in 5 popular applications. We found one performance problem that was confirmed and fixed, four problems that were confirmed, one confirmed problem that was a duplicate of an older report, and three more potential performance problems that developers agree may be improved.
{"title":"SunCat: helping developers understand and predict performance problems in smartphone applications","authors":"Adrian Nistor, Lenin Ravindranath","doi":"10.1145/2610384.2610410","DOIUrl":"https://doi.org/10.1145/2610384.2610410","url":null,"abstract":"The number of smartphones shipped in 2014 will be four times larger than the number of PCs. Compared to PCs, smartphones have limited computing resources, and smartphone applications are more prone to performance problems. Traditionally, developers use profilers to detect performance problems by running applications with relatively large inputs. Unfortunately, for smartphone applications, the developer cannot easily control the input, because smartphone applications interact heavily with the environment. \u0000 Given a run on a small input, how can a developer detect performance problems that would occur for a run with large input? We present SUNCAT, a novel technique that helps developers understand and predict performance problems in smartphone applications. The developer runs the application using a common input, typically small, and SUNCAT presents a prioritized list of repetition patterns that summarize the current run plus additional information to help the developer understand how these patterns may grow in the future runs with large inputs. We implemented SUNCAT for Windows Phone systems and used it to understand the performance characteristics of 29 usage scenarios in 5 popular applications. We found one performance problem that was confirmed and fixed, four problems that were confirmed, one confirmed problem that was a duplicate of an older report, and three more potential performance problems that developers agree may be improved.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"78 1","pages":"282-292"},"PeriodicalIF":0.0,"publicationDate":"2014-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81629227","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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