Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single collection, but real-world programs often track multiple related collections and aggregate properties such as sums, counts, minimums, and maximums. This paper shows how to synthesize data structures that track subsets and aggregations of multiple related collections. Our technique decomposes the synthesis task into alternating steps of query synthesis and incrementalization. The query synthesis step implements pure operations over the data structure state by leveraging existing enumerative synthesis techniques, specialized to the data structures domain. The incrementalization step implements imperative state modifications by re-framing them as fresh queries that determine what to change, coupled with a small amount of code to apply the change. As an added benefit of this approach over previous work, the synthesized data structure is optimized for not only the queries in the specification but also the required update operations. We have evaluated our approach in four large case studies, demonstrating that these extensions are broadly applicable.
{"title":"Generalized Data Structure Synthesis","authors":"Calvin Loncaric, Michael D. Ernst, E. Torlak","doi":"10.1145/3180155.3180211","DOIUrl":"https://doi.org/10.1145/3180155.3180211","url":null,"abstract":"Data structure synthesis is the task of generating data structure implementations from high-level specifications. Recent work in this area has shown potential to save programmer time and reduce the risk of defects. Existing techniques focus on data structures for manipulating subsets of a single collection, but real-world programs often track multiple related collections and aggregate properties such as sums, counts, minimums, and maximums. This paper shows how to synthesize data structures that track subsets and aggregations of multiple related collections. Our technique decomposes the synthesis task into alternating steps of query synthesis and incrementalization. The query synthesis step implements pure operations over the data structure state by leveraging existing enumerative synthesis techniques, specialized to the data structures domain. The incrementalization step implements imperative state modifications by re-framing them as fresh queries that determine what to change, coupled with a small amount of code to apply the change. As an added benefit of this approach over previous work, the synthesized data structure is optimized for not only the queries in the specification but also the required update operations. We have evaluated our approach in four large case studies, demonstrating that these extensions are broadly applicable.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"19 1","pages":"958-968"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75342901","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}
B. Lin, Fiorella Zampetti, G. Bavota, M. D. Penta, Michele Lanza, R. Oliveto
Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the specific usage context. We describe our experience in building a software library recommender exploiting crowdsourced opinions mined from Stack Overflow (e.g., what is the sentiment of developers about the usability of a library). To reach our goal, we retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an effort- and time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of these tools on a variety of SE datasets. Our results should warn the research community about the strong limitations of current sentiment analysis tools.
{"title":"Sentiment Analysis for Software Engineering: How Far Can We Go?","authors":"B. Lin, Fiorella Zampetti, G. Bavota, M. D. Penta, Michele Lanza, R. Oliveto","doi":"10.1145/3180155.3180195","DOIUrl":"https://doi.org/10.1145/3180155.3180195","url":null,"abstract":"Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the specific usage context. We describe our experience in building a software library recommender exploiting crowdsourced opinions mined from Stack Overflow (e.g., what is the sentiment of developers about the usability of a library). To reach our goal, we retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an effort- and time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of these tools on a variety of SE datasets. Our results should warn the research community about the strong limitations of current sentiment analysis tools.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"33 1","pages":"94-104"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80051548","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}
Cuiyun Gao, Jichuan Zeng, Michael R. Lyu, Irwin King
Detecting emerging issues (e.g., new bugs) timely and precisely is crucial for developers to update their apps. App reviews provide an opportunity to proactively collect user complaints and promptly improve apps' user experience, in terms of bug fixing and feature refinement. However, the tremendous quantities of reviews and noise words (e.g., misspelled words) increase the difficulties in accurately identifying newly-appearing app issues. In this paper, we propose a novel and automated framework IDEA, which aims to IDentify Emerging App issues effectively based on online review analysis. We evaluate IDEA on six popular apps from Google Play and Apple's App Store, employing the official app changelogs as our ground truth. Experiment results demonstrate the effectiveness of IDEA in identifying emerging app issues. Feedback from engineers and product managers shows that 88.9% of them think that the identified issues can facilitate app development in practice. Moreover, we have successfully applied IDEA to several products of Tencent, which serve hundreds of millions of users.
{"title":"Online App Review Analysis for Identifying Emerging Issues","authors":"Cuiyun Gao, Jichuan Zeng, Michael R. Lyu, Irwin King","doi":"10.1145/3180155.3180218","DOIUrl":"https://doi.org/10.1145/3180155.3180218","url":null,"abstract":"Detecting emerging issues (e.g., new bugs) timely and precisely is crucial for developers to update their apps. App reviews provide an opportunity to proactively collect user complaints and promptly improve apps' user experience, in terms of bug fixing and feature refinement. However, the tremendous quantities of reviews and noise words (e.g., misspelled words) increase the difficulties in accurately identifying newly-appearing app issues. In this paper, we propose a novel and automated framework IDEA, which aims to IDentify Emerging App issues effectively based on online review analysis. We evaluate IDEA on six popular apps from Google Play and Apple's App Store, employing the official app changelogs as our ground truth. Experiment results demonstrate the effectiveness of IDEA in identifying emerging app issues. Feedback from engineers and product managers shows that 88.9% of them think that the identified issues can facilitate app development in practice. Moreover, we have successfully applied IDEA to several products of Tencent, which serve hundreds of millions of users.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"6 1","pages":"48-58"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84886560","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}
David Trabish, Andrea Mattavelli, N. Rinetzky, Cristian Cadar
Symbolic execution is a powerful program analysis technique that systematically explores multiple program paths. However, despite important technical advances, symbolic execution often struggles to reach deep parts of the code due to the well-known path explosion problem and constraint solving limitations. In this paper, we propose chopped symbolic execution, a novel form of symbolic execution that allows users to specify uninter-esting parts of the code to exclude during the analysis, thus only targeting the exploration to paths of importance. However, the excluded parts are not summarily ignored, as this may lead to both false positives and false negatives. Instead, they are executed lazily, when their effect may be observable by code under anal-ysis. Chopped symbolic execution leverages various on-demand static analyses at runtime to automatically exclude code fragments while resolving their side effects, thus avoiding expensive manual annotations and imprecision. Our preliminary results show that the approach can effectively improve the effectiveness of symbolic execution in several different scenarios, including failure reproduction and test suite augmentation.
{"title":"Chopped Symbolic Execution","authors":"David Trabish, Andrea Mattavelli, N. Rinetzky, Cristian Cadar","doi":"10.1145/3180155.3180251","DOIUrl":"https://doi.org/10.1145/3180155.3180251","url":null,"abstract":"Symbolic execution is a powerful program analysis technique that systematically explores multiple program paths. However, despite important technical advances, symbolic execution often struggles to reach deep parts of the code due to the well-known path explosion problem and constraint solving limitations. In this paper, we propose chopped symbolic execution, a novel form of symbolic execution that allows users to specify uninter-esting parts of the code to exclude during the analysis, thus only targeting the exploration to paths of importance. However, the excluded parts are not summarily ignored, as this may lead to both false positives and false negatives. Instead, they are executed lazily, when their effect may be observable by code under anal-ysis. Chopped symbolic execution leverages various on-demand static analyses at runtime to automatically exclude code fragments while resolving their side effects, thus avoiding expensive manual annotations and imprecision. Our preliminary results show that the approach can effectively improve the effectiveness of symbolic execution in several different scenarios, including failure reproduction and test suite augmentation.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"73 1","pages":"350-360"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85706586","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 optimal feature selection problem in software product line is typically addressed by the approaches based on Indicator-based Evolutionary Algorithm (IBEA). In this study, we frst expose the mathematical nature of this problem — multi-objective binary integer linear programming. Then, we implement/propose three mathematical programming approaches to solve this problem at di?erent scales. For small-scale problems (roughly, less than 100 features), we implement two established approaches to fnd all exact solutions. For medium-to-large problems (roughly, more than 100 features), we propose one efcient approach that can generate a representation of the entire Pareto front in linear time complexity. The empirical results show that our proposed method can fnd signifcantly more non-dominated solutions in similar or less execution time, in comparison with IBEA and its recent enhancement (i.e., IBED that combines IBEA and Di?erential Evolution).
{"title":"Multi-objective Integer Programming Approaches for Solving Optimal Feature Selection Problem: A New Perspective on Multi-objective Optimization Problems in SBSE","authors":"Yinxing Xue, Yanfu Li","doi":"10.1145/3180155.3180257","DOIUrl":"https://doi.org/10.1145/3180155.3180257","url":null,"abstract":"The optimal feature selection problem in software product line is typically addressed by the approaches based on Indicator-based Evolutionary Algorithm (IBEA). In this study, we frst expose the mathematical nature of this problem — multi-objective binary integer linear programming. Then, we implement/propose three mathematical programming approaches to solve this problem at di?erent scales. For small-scale problems (roughly, less than 100 features), we implement two established approaches to fnd all exact solutions. For medium-to-large problems (roughly, more than 100 features), we propose one efcient approach that can generate a representation of the entire Pareto front in linear time complexity. The empirical results show that our proposed method can fnd signifcantly more non-dominated solutions in similar or less execution time, in comparison with IBEA and its recent enhancement (i.e., IBED that combines IBEA and Di?erential Evolution).","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"315 1","pages":"1231-1242"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77782627","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}
S. Krieter, Thomas Thüm, Sandro Schulze, R. Schröter, G. Saake
Highly-configurable systems encompass thousands of interdependent configuration options, which require a non-trivial configuration process. Decision propagation enables a backtracking-free configuration process by computing values implied by user decisions. However, employing decision propagation for large-scale systems is a time-consuming task and, thus, can be a bottleneck in interactive configuration processes and analyses alike. We propose modal implication graphs to improve the performance of decision propagation by precomputing intermediate values used in the process. Our evaluation results show a significant improvement over state-of-the-art algorithms for 120 real-world systems.
{"title":"Propagating Configuration Decisions with Modal Implication Graphs","authors":"S. Krieter, Thomas Thüm, Sandro Schulze, R. Schröter, G. Saake","doi":"10.1145/3180155.3180159","DOIUrl":"https://doi.org/10.1145/3180155.3180159","url":null,"abstract":"Highly-configurable systems encompass thousands of interdependent configuration options, which require a non-trivial configuration process. Decision propagation enables a backtracking-free configuration process by computing values implied by user decisions. However, employing decision propagation for large-scale systems is a time-consuming task and, thus, can be a bottleneck in interactive configuration processes and analyses alike. We propose modal implication graphs to improve the performance of decision propagation by precomputing intermediate values used in the process. Our evaluation results show a significant improvement over state-of-the-art algorithms for 120 real-world systems.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"21 1","pages":"898-909"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80118624","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}
D. Germán, G. Robles, Germán Poo-Caamaño, Xin Yang, Hajimu Iida, Katsuro Inoue
Modern code reviews improve the quality of software products. Although modern code reviews rely heavily on human interactions, little is known regarding whether they are performed fairly. Fairness plays a role in any process where decisions that affect others are made. When a system is perceived to be unfair, it affects negatively the productivity and motivation of its participants. In this paper, using fairness theory we create a framework that describes how fairness affects modern code reviews. To demonstrate its applicability, and the importance of fairness in code reviews, we conducted an em-pirical study that asked developers of a large industrial open source ecosystem (OpenStack) what their perceptions are regarding fairness in their code reviewing process. Our study shows that, in general, the code review process in OpenStack is perceived as fair; however, a significant portion of respondents perceive it as unfair. We also show that the variability in the way they prioritize code reviews signals a lack of consistency and the existence of bias (potentially increasing the perception of unfairness). The contributions of this paper are: (1) we propose a framework—based on fairness theory—for studying and managing social behaviour in modern code reviews, (2) we provide support for the framework through the results of a case study on a large industrial-backed open source project, (3) we present evidence that fairness is an issue in the code review process of a large open source ecosystem, and, (4) we present a set of guidelines for practitioners to address unfairness in modern code reviews.
{"title":"\"Was My Contribution Fairly Reviewed?\" A Framework to Study the Perception of Fairness in Modern Code Reviews","authors":"D. Germán, G. Robles, Germán Poo-Caamaño, Xin Yang, Hajimu Iida, Katsuro Inoue","doi":"10.1145/3180155.3180217","DOIUrl":"https://doi.org/10.1145/3180155.3180217","url":null,"abstract":"Modern code reviews improve the quality of software products. Although modern code reviews rely heavily on human interactions, little is known regarding whether they are performed fairly. Fairness plays a role in any process where decisions that affect others are made. When a system is perceived to be unfair, it affects negatively the productivity and motivation of its participants. In this paper, using fairness theory we create a framework that describes how fairness affects modern code reviews. To demonstrate its applicability, and the importance of fairness in code reviews, we conducted an em-pirical study that asked developers of a large industrial open source ecosystem (OpenStack) what their perceptions are regarding fairness in their code reviewing process. Our study shows that, in general, the code review process in OpenStack is perceived as fair; however, a significant portion of respondents perceive it as unfair. We also show that the variability in the way they prioritize code reviews signals a lack of consistency and the existence of bias (potentially increasing the perception of unfairness). The contributions of this paper are: (1) we propose a framework—based on fairness theory—for studying and managing social behaviour in modern code reviews, (2) we provide support for the framework through the results of a case study on a large industrial-backed open source project, (3) we present evidence that fairness is an issue in the code review process of a large open source ecosystem, and, (4) we present a set of guidelines for practitioners to address unfairness in modern code reviews.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"54 1","pages":"523-534"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86868459","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}
Zero-day Use-After-Free (UAF) vulnerabilities are increasingly popular and highly dangerous, but few mitigations exist. We introduce a new pointer-analysis-based static analysis, CRed, for finding UAF bugs in multi-MLOC C source code efficiently and effectively. Cred achieves this by making three advances: (i) a spatio-temporal context reduction technique for scaling down soundly and precisely the exponential number of contexts that would otherwise be considered at a pair of free and use sites, (ii) a multi-stage analysis for filtering out false alarms efficiently, and (iii) a path-sensitive demand-driven approach for finding the points-to information required. We have implemented CRed in LLVM-3.8.0 and compared it with four different state-of-the-art static tools: CBMC (model checking), Clang (abstract interpretation), Coccinelle (pattern matching), and Supa (pointer analysis) using all the C test cases in Juliet Test Suite (JTS) and 10 open-source C applications. For the ground-truth validated with JTS, CRed detects all the 138 known UAF bugs as CBMC and Supa do while Clang and Coccinelle miss some bugs, with no false alarms from any tool. For practicality validated with the 10 applications (totaling 3+ MLOC), CRed reports 132 warnings including 85 bugs in 7.6 hours while the existing tools are either unscalable by terminating within 3 days only for one application (CBMC) or impractical by finding virtually no bugs (Clang and Coccinelle) or issuing an excessive number of false alarms (Supa).
{"title":"Spatio-Temporal Context Reduction: A Pointer-Analysis-Based Static Approach for Detecting Use-After-Free Vulnerabilities","authors":"Hua Yan, Yulei Sui, Shiping Chen, Jingling Xue","doi":"10.1145/3180155.3180178","DOIUrl":"https://doi.org/10.1145/3180155.3180178","url":null,"abstract":"Zero-day Use-After-Free (UAF) vulnerabilities are increasingly popular and highly dangerous, but few mitigations exist. We introduce a new pointer-analysis-based static analysis, CRed, for finding UAF bugs in multi-MLOC C source code efficiently and effectively. Cred achieves this by making three advances: (i) a spatio-temporal context reduction technique for scaling down soundly and precisely the exponential number of contexts that would otherwise be considered at a pair of free and use sites, (ii) a multi-stage analysis for filtering out false alarms efficiently, and (iii) a path-sensitive demand-driven approach for finding the points-to information required. We have implemented CRed in LLVM-3.8.0 and compared it with four different state-of-the-art static tools: CBMC (model checking), Clang (abstract interpretation), Coccinelle (pattern matching), and Supa (pointer analysis) using all the C test cases in Juliet Test Suite (JTS) and 10 open-source C applications. For the ground-truth validated with JTS, CRed detects all the 138 known UAF bugs as CBMC and Supa do while Clang and Coccinelle miss some bugs, with no false alarms from any tool. For practicality validated with the 10 applications (totaling 3+ MLOC), CRed reports 132 warnings including 85 bugs in 7.6 hours while the existing tools are either unscalable by terminating within 3 days only for one application (CBMC) or impractical by finding virtually no bugs (Clang and Coccinelle) or issuing an excessive number of false alarms (Supa).","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"68 1","pages":"327-337"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88062504","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}
Jun-Wei Lin, Reyhaneh Jabbarvand, Joshua Garcia, S. Malek
Multi-criteria test-suite minimization aims to remove redundant test cases from a test suite based on some criteria such as code coverage, while trying to optimally maintain the capability of the reduced suite based on other criteria such as fault-detection effectiveness. Existing techniques addressing this problem with integer linear programming claim to produce optimal solutions. However, the multi-criteria test-suite minimization problem is inherently nonlinear, due to the fact that test cases are often dependent on each other in terms of test-case criteria. In this paper, we propose a framework that formulates the multi-criteria test-suite minimization problem as an integer nonlinear programming problem. To solve this problem optimally, we programmatically transform this nonlinear problem into a linear one and then solve the problem using modern linear solvers. We have implemented our framework as a tool, called Nemo, that supports a number of modern linear and nonlinear solvers. We have evaluated Nemo with a publicly available dataset and minimization problems involving multiple criteria including statement coverage, fault-revealing capability, and test execution time. The experimental results show that Nemo can be used to efficiently find an optimal solution for multi-criteria test-suite minimization problems with modern solvers, and the optimal solutions outperform the suboptimal ones by up to 164.29% in terms of the criteria considered in the problem.
{"title":"Nemo: Multi-criteria Test-Suite Minimization with Integer Nonlinear Programming","authors":"Jun-Wei Lin, Reyhaneh Jabbarvand, Joshua Garcia, S. Malek","doi":"10.1145/3180155.3180174","DOIUrl":"https://doi.org/10.1145/3180155.3180174","url":null,"abstract":"Multi-criteria test-suite minimization aims to remove redundant test cases from a test suite based on some criteria such as code coverage, while trying to optimally maintain the capability of the reduced suite based on other criteria such as fault-detection effectiveness. Existing techniques addressing this problem with integer linear programming claim to produce optimal solutions. However, the multi-criteria test-suite minimization problem is inherently nonlinear, due to the fact that test cases are often dependent on each other in terms of test-case criteria. In this paper, we propose a framework that formulates the multi-criteria test-suite minimization problem as an integer nonlinear programming problem. To solve this problem optimally, we programmatically transform this nonlinear problem into a linear one and then solve the problem using modern linear solvers. We have implemented our framework as a tool, called Nemo, that supports a number of modern linear and nonlinear solvers. We have evaluated Nemo with a publicly available dataset and minimization problems involving multiple criteria including statement coverage, fault-revealing capability, and test execution time. The experimental results show that Nemo can be used to efficiently find an optimal solution for multi-criteria test-suite minimization problems with modern solvers, and the optimal solutions outperform the suboptimal ones by up to 164.29% in terms of the criteria considered in the problem.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"15 1","pages":"1039-1049"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89776528","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}
Chunyang Chen, Ting Su, Guozhu Meng, Zhenchang Xing, Yang Liu
A GUI skeleton is the starting point for implementing a UI design image. To obtain a GUI skeleton from a UI design image, developers have to visually understand UI elements and their spatial layout in the image, and then translate this understanding into proper GUI components and their compositions. Automating this visual understanding and translation would be beneficial for bootstraping mobile GUI implementation, but it is a challenging task due to the diversity of UI designs and the complexity of GUI skeletons to generate. Existing tools are rigid as they depend on heuristically-designed visual understanding and GUI generation rules. In this paper, we present a neural machine translator that combines recent advances in computer vision and machine translation for translating a UI design image into a GUI skeleton. Our translator learns to extract visual features in UI images, encode these features' spatial layouts, and generate GUI skeletons in a unified neural network framework, without requiring manual rule development. For training our translator, we develop an automated GUI exploration method to automatically collect large-scale UI data from real-world applications. We carry out extensive experiments to evaluate the accuracy, generality and usefulness of our approach.
{"title":"From UI Design Image to GUI Skeleton: A Neural Machine Translator to Bootstrap Mobile GUI Implementation","authors":"Chunyang Chen, Ting Su, Guozhu Meng, Zhenchang Xing, Yang Liu","doi":"10.1145/3180155.3180240","DOIUrl":"https://doi.org/10.1145/3180155.3180240","url":null,"abstract":"A GUI skeleton is the starting point for implementing a UI design image. To obtain a GUI skeleton from a UI design image, developers have to visually understand UI elements and their spatial layout in the image, and then translate this understanding into proper GUI components and their compositions. Automating this visual understanding and translation would be beneficial for bootstraping mobile GUI implementation, but it is a challenging task due to the diversity of UI designs and the complexity of GUI skeletons to generate. Existing tools are rigid as they depend on heuristically-designed visual understanding and GUI generation rules. In this paper, we present a neural machine translator that combines recent advances in computer vision and machine translation for translating a UI design image into a GUI skeleton. Our translator learns to extract visual features in UI images, encode these features' spatial layouts, and generate GUI skeletons in a unified neural network framework, without requiring manual rule development. For training our translator, we develop an automated GUI exploration method to automatically collect large-scale UI data from real-world applications. We carry out extensive experiments to evaluate the accuracy, generality and usefulness of our approach.","PeriodicalId":6560,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)","volume":"92 1","pages":"665-676"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73442203","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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