It is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.
{"title":"Effective API Recommendation without Historical Software Repositories","authors":"Xiaoyu Liu, LiGuo Huang, Vincent Ng","doi":"10.1145/3238147.3238216","DOIUrl":"https://doi.org/10.1145/3238147.3238216","url":null,"abstract":"It is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"145 1","pages":"282-292"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82270719","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 maintenance is becoming more challenging with the increased complexity of the software and the frequently applied changes. Performing impact analysis before the actual implementation of a change is a crucial task during system maintenance. While many tools and techniques are available to measure the impact of a change at the code level, only a few research work is done to measure the impact of a change at an earlier stage in the development process. This work introduces an approach to measure the impact of a change at the model level.
{"title":"Automatically Quantifying the Impact of a Change in Systems (Journal-First Abstract)","authors":"Nada Almasri, L. Tahat, B. Korel","doi":"10.1145/3238147.3241984","DOIUrl":"https://doi.org/10.1145/3238147.3241984","url":null,"abstract":"Software maintenance is becoming more challenging with the increased complexity of the software and the frequently applied changes. Performing impact analysis before the actual implementation of a change is a crucial task during system maintenance. While many tools and techniques are available to measure the impact of a change at the code level, only a few research work is done to measure the impact of a change at an earlier stage in the development process. This work introduces an approach to measure the impact of a change at the model level.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"10 1","pages":"952-952"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85544659","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}
Refactoring is nowadays widely adopted in the industry because bad design decisions can be very costly and extremely risky. On the one hand, automated refactoring does not always lead to the desired design. On the other hand, manual refactoring is error-prone, time-consuming and not practical for radical changes. Thus, recent research trends in the field focused on integrating developers feedback into automated refactoring recommendations because developers understand the problem domain intuitively and may have a clear target design in mind. However, this interactive process can be repetitive, expensive, and tedious since developers must evaluate recommended refactorings, and adapt them to the targeted design especially in large systems where the number of possible strategies can grow exponentially. In this paper, we propose an interactive approach combining the use of multi-objective and unsupervised learning to reduce the developer's interaction effort when refactoring systems. We generate, first, using multi-objective search different possible refactoring strategies by finding a trade-off between several conflicting quality attributes. Then, an unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. The feedback from the developer, both at the cluster and solution levels, are used to automatically generate constraints to reduce the search space in the next iterations and focus on the region of developer preferences. We selected 14 active developers to manually evaluate the effectiveness our tool on 5 open source projects and one industrial system. The results show that the participants found their desired refactorings faster and more accurate than the current state of the art.
{"title":"Reducing Interactive Refactoring Effort via Clustering-Based Multi-objective Search","authors":"Vahid Alizadeh, M. Kessentini","doi":"10.1145/3238147.3238217","DOIUrl":"https://doi.org/10.1145/3238147.3238217","url":null,"abstract":"Refactoring is nowadays widely adopted in the industry because bad design decisions can be very costly and extremely risky. On the one hand, automated refactoring does not always lead to the desired design. On the other hand, manual refactoring is error-prone, time-consuming and not practical for radical changes. Thus, recent research trends in the field focused on integrating developers feedback into automated refactoring recommendations because developers understand the problem domain intuitively and may have a clear target design in mind. However, this interactive process can be repetitive, expensive, and tedious since developers must evaluate recommended refactorings, and adapt them to the targeted design especially in large systems where the number of possible strategies can grow exponentially. In this paper, we propose an interactive approach combining the use of multi-objective and unsupervised learning to reduce the developer's interaction effort when refactoring systems. We generate, first, using multi-objective search different possible refactoring strategies by finding a trade-off between several conflicting quality attributes. Then, an unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. The feedback from the developer, both at the cluster and solution levels, are used to automatically generate constraints to reduce the search space in the next iterations and focus on the region of developer preferences. We selected 14 active developers to manually evaluate the effectiveness our tool on 5 open source projects and one industrial system. The results show that the participants found their desired refactorings faster and more accurate than the current state of the art.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"44 3","pages":"464-474"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91492481","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}
Xiaoping Zhou, Xin Peng, Tao Xie, Jun Sun, Wenhai Li, Chao Ji, Dan Ding
Debugging microservice systems involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, in this paper, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations) for effective debugging. Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas following the idea of delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines a set of dimensions that can affect the execution of microservice systems. Our experimental study on a medium-size microservice benchmark system shows that our approach can effectively identify failure-inducing deltas that help diagnose the root causes.
{"title":"Delta Debugging Microservice Systems","authors":"Xiaoping Zhou, Xin Peng, Tao Xie, Jun Sun, Wenhai Li, Chao Ji, Dan Ding","doi":"10.1145/3238147.3240730","DOIUrl":"https://doi.org/10.1145/3238147.3240730","url":null,"abstract":"Debugging microservice systems involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, in this paper, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations) for effective debugging. Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas following the idea of delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines a set of dimensions that can affect the execution of microservice systems. Our experimental study on a medium-size microservice benchmark system shows that our approach can effectively identify failure-inducing deltas that help diagnose the root causes.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"39 1","pages":"802-807"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80161115","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}
Descartes is a tool that implements extreme mutation operators and aims at finding pseudo-tested methods in Java projects. It leverages the efficient transformation and runtime features of PITest. The demonstration compares Descartes with Gregor, the default mutation engine provided by PITest, in a set of real open source projects. It considers the execution time, number of mutants created and the relationship between the mutation scores produced by both engines. It provides some insights on the main features exposed by Descartes.
{"title":"Descartes: A PITest Engine to Detect Pseudo-Tested Methods: Tool Demonstration","authors":"O. Vera-Pérez, Monperrus Martin, B. Baudry","doi":"10.1145/3238147.3240474","DOIUrl":"https://doi.org/10.1145/3238147.3240474","url":null,"abstract":"Descartes is a tool that implements extreme mutation operators and aims at finding pseudo-tested methods in Java projects. It leverages the efficient transformation and runtime features of PITest. The demonstration compares Descartes with Gregor, the default mutation engine provided by PITest, in a set of real open source projects. It considers the execution time, number of mutants created and the relationship between the mutation scores produced by both engines. It provides some insights on the main features exposed by Descartes.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"908-911"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83786186","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}
Modern distributed version control systems, such as Git, offer support for branching — the possibility to develop parts of software outside the master trunk. Consideration of the repository structure in Mining Software Repository (MSR) studies requires a thorough approach to mining, but there is no well-documented, widespread methodology regarding the handling of merge commits and branches. Moreover, there is still a lack of knowledge of the extent to which considering branches during MSR studies impacts the results of the studies. In this study, we set out to evaluate the importance of proper handling of branches when calculating file modification histories. We analyze over 1,400 Git repositories of four open source ecosystems and compute modification histories for over two million files, using two different algorithms. One algorithm only follows the first parent of each commit when traversing the repository, the other returns the full modification history of a file across all branches. We show that the two algorithms consistently deliver different results, but the scale of the difference varies across projects and ecosystems. Further, we evaluate the importance of accurate mining of file histories by comparing the performance of common techniques that rely on file modification history — reviewer recommendation, change recommendation, and defect prediction — for two algorithms of file history retrieval. We find that considering full file histories leads to an increase in the techniques' performance that is rather modest.
{"title":"Mining File Histories: Should We Consider Branches?","authors":"V. Kovalenko, Fabio Palomba, Alberto Bacchelli","doi":"10.1145/3238147.3238169","DOIUrl":"https://doi.org/10.1145/3238147.3238169","url":null,"abstract":"Modern distributed version control systems, such as Git, offer support for branching — the possibility to develop parts of software outside the master trunk. Consideration of the repository structure in Mining Software Repository (MSR) studies requires a thorough approach to mining, but there is no well-documented, widespread methodology regarding the handling of merge commits and branches. Moreover, there is still a lack of knowledge of the extent to which considering branches during MSR studies impacts the results of the studies. In this study, we set out to evaluate the importance of proper handling of branches when calculating file modification histories. We analyze over 1,400 Git repositories of four open source ecosystems and compute modification histories for over two million files, using two different algorithms. One algorithm only follows the first parent of each commit when traversing the repository, the other returns the full modification history of a file across all branches. We show that the two algorithms consistently deliver different results, but the scale of the difference varies across projects and ecosystems. Further, we evaluate the importance of accurate mining of file histories by comparing the performance of common techniques that rely on file modification history — reviewer recommendation, change recommendation, and defect prediction — for two algorithms of file history retrieval. We find that considering full file histories leads to an increase in the techniques' performance that is rather modest.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"19 1","pages":"202-213"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88115509","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}
We present SRCIROR (pronounced “sorcerer”), a toolset for performing mutation testing at the levels of C/C++ source code (SRC) and the LLVM compiler intermediate representation (IR). At the SRC level, SRCIROR identifies program constructs for mutation by pattern-matching on the Clang AST. At the IR level, SRCIROR directly mutates the LLVM IR instructions through LLVM passes. Our implementation enables SRCIROR to (1) handle any program that Clang can handle, extending to large programs with a minimal overhead, and (2) have a small percentage of invalid mutants that do not compile. SRCIROR enables performing mutation testing using the same classes of mutation operators at both the SRC and IR levels, and it is easily extensible to support more operators. In addition, SRCIROR can collect coverage to generate mutants only for covered code elements. Our tool is publicly available on GitHub (https://github.com/TestingResearchIllinois/srciror). We evaluate SRCIROR on Coreutils subjects. Our evaluation shows interesting differences between SRC and IR, demonstrating the value of SR-CIROR in enabling mutation testing research across different levels of code representation.
{"title":"SRCIROR: A Toolset for Mutation Testing of C Source Code and LLVM Intermediate Representation","authors":"Farah Hariri, A. Shi","doi":"10.1145/3238147.3240482","DOIUrl":"https://doi.org/10.1145/3238147.3240482","url":null,"abstract":"We present SRCIROR (pronounced “sorcerer”), a toolset for performing mutation testing at the levels of C/C++ source code (SRC) and the LLVM compiler intermediate representation (IR). At the SRC level, SRCIROR identifies program constructs for mutation by pattern-matching on the Clang AST. At the IR level, SRCIROR directly mutates the LLVM IR instructions through LLVM passes. Our implementation enables SRCIROR to (1) handle any program that Clang can handle, extending to large programs with a minimal overhead, and (2) have a small percentage of invalid mutants that do not compile. SRCIROR enables performing mutation testing using the same classes of mutation operators at both the SRC and IR levels, and it is easily extensible to support more operators. In addition, SRCIROR can collect coverage to generate mutants only for covered code elements. Our tool is publicly available on GitHub (https://github.com/TestingResearchIllinois/srciror). We evaluate SRCIROR on Coreutils subjects. Our evaluation shows interesting differences between SRC and IR, demonstrating the value of SR-CIROR in enabling mutation testing research across different levels of code representation.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"10 1","pages":"860-863"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85183959","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}
Renzo Degiovanni, F. Molina, Germán Regis, Nazareno Aguirre
Goal-conflict analysis has been widely used as an abstraction for risk analysis in goal-oriented requirements engineering approaches. In this context, where the expected behaviour of the system-to-be is captured in terms of domain properties and goals, identifying combinations of circumstances that may make the goals diverge, i.e., not to be satisfied as a whole, is of most importance. Various approaches have been proposed in order to automatically identify boundary conditions, i.e., formulas capturing goal-divergent situations, but they either apply only to some specific goal expressions, or are affected by scalability issues that make them applicable only to relatively small specifications. In this paper, we present a novel approach to automatically identify boundary conditions, using evolutionary computation. More precisely, we develop a genetic algorithm that, given the LTL formulation of the domain properties and the goals, it searches for formulas that capture divergences in the specification. We exploit a modern LTL satisfiability checker to successfully guide our genetic algorithm to the solutions. We assess our technique on a set of case studies, and show that our genetic algorithm is able to find boundary conditions that cannot be generated by related approaches, and is able to efficiently scale to LTL specifications that other approaches are unable to deal with.
{"title":"A Genetic Algorithm for Goal-Conflict Identification","authors":"Renzo Degiovanni, F. Molina, Germán Regis, Nazareno Aguirre","doi":"10.1145/3238147.3238220","DOIUrl":"https://doi.org/10.1145/3238147.3238220","url":null,"abstract":"Goal-conflict analysis has been widely used as an abstraction for risk analysis in goal-oriented requirements engineering approaches. In this context, where the expected behaviour of the system-to-be is captured in terms of domain properties and goals, identifying combinations of circumstances that may make the goals diverge, i.e., not to be satisfied as a whole, is of most importance. Various approaches have been proposed in order to automatically identify boundary conditions, i.e., formulas capturing goal-divergent situations, but they either apply only to some specific goal expressions, or are affected by scalability issues that make them applicable only to relatively small specifications. In this paper, we present a novel approach to automatically identify boundary conditions, using evolutionary computation. More precisely, we develop a genetic algorithm that, given the LTL formulation of the domain properties and the goals, it searches for formulas that capture divergences in the specification. We exploit a modern LTL satisfiability checker to successfully guide our genetic algorithm to the solutions. We assess our technique on a set of case studies, and show that our genetic algorithm is able to find boundary conditions that cannot be generated by related approaches, and is able to efficiently scale to LTL specifications that other approaches are unable to deal with.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"520-531"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83167187","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}
Hashim Sharif, Muhammad Abubakar, Ashish Gehani, Fareed Zaffar
With the proliferation of new hardware architectures and ever-evolving user requirements, the software stack is becoming increasingly bloated. In practice, only a limited subset of the supported functionality is utilized in a particular usage context, thereby presenting an opportunity to eliminate unused features. In the past, program specialization has been proposed as a mechanism for enabling automatic software debloating. In this work, we show how existing program specialization techniques lack the analyses required for providing code simplification for real-world programs. We present an approach that uses stronger analysis techniques to take advantage of constant configuration data, thereby enabling more effective debloating. We developed Trimmer, an application specialization tool that leverages user-provided configuration data to specialize an application to its deployment context. The specialization process attempts to eliminate the application functionality that is unused in the user-defined context. Our evaluation demonstrates Trimmer can effectively reduce code bloat. For 13 applications spanning various domains, we observe a mean binary size reduction of 21% and a maximum reduction of 75%. We also show specialization reduces the surface for code-reuse attacks by reducing the number of exploitable gadgets. For the evaluated programs, we observe a 20% mean reduction in the total gadget count and a maximum reduction of 87%.
{"title":"Trimmer: Application Specialization for Code Debloating","authors":"Hashim Sharif, Muhammad Abubakar, Ashish Gehani, Fareed Zaffar","doi":"10.1145/3238147.3238160","DOIUrl":"https://doi.org/10.1145/3238147.3238160","url":null,"abstract":"With the proliferation of new hardware architectures and ever-evolving user requirements, the software stack is becoming increasingly bloated. In practice, only a limited subset of the supported functionality is utilized in a particular usage context, thereby presenting an opportunity to eliminate unused features. In the past, program specialization has been proposed as a mechanism for enabling automatic software debloating. In this work, we show how existing program specialization techniques lack the analyses required for providing code simplification for real-world programs. We present an approach that uses stronger analysis techniques to take advantage of constant configuration data, thereby enabling more effective debloating. We developed Trimmer, an application specialization tool that leverages user-provided configuration data to specialize an application to its deployment context. The specialization process attempts to eliminate the application functionality that is unused in the user-defined context. Our evaluation demonstrates Trimmer can effectively reduce code bloat. For 13 applications spanning various domains, we observe a mean binary size reduction of 21% and a maximum reduction of 75%. We also show specialization reduces the surface for code-reuse attacks by reducing the number of exploitable gadgets. For the evaluated programs, we observe a 20% mean reduction in the total gadget count and a maximum reduction of 87%.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"64 1","pages":"329-339"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80729556","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}
Simone Scalabrino, Giovanni Grano, Dario Di Nucci, Michele Guerra, A. De Lucia, H. Gall, R. Oliveto
Automatically generating test cases plays an important role to reduce the time spent by developers during the testing phase. In last years, several approaches have been proposed to tackle such a problem: amongst others, search-based techniques have been shown to be particularly promising. In this paper we describe Ocelot, a search-based tool for the automatic generation of test cases in C. Ocelot allows practitioners to write skeletons of test cases for their programs and researchers to easily implement and experiment new approaches for automatic test-data generation. We show that Ocelot achieves a higher coverage compared to a competitive tool in 81% of the cases. Ocelot is publicly available to support both researchers and practitioners.
{"title":"OCELOT: A Search-Based Test-Data Generation Tool for C","authors":"Simone Scalabrino, Giovanni Grano, Dario Di Nucci, Michele Guerra, A. De Lucia, H. Gall, R. Oliveto","doi":"10.1145/3238147.3240477","DOIUrl":"https://doi.org/10.1145/3238147.3240477","url":null,"abstract":"Automatically generating test cases plays an important role to reduce the time spent by developers during the testing phase. In last years, several approaches have been proposed to tackle such a problem: amongst others, search-based techniques have been shown to be particularly promising. In this paper we describe Ocelot, a search-based tool for the automatic generation of test cases in C. Ocelot allows practitioners to write skeletons of test cases for their programs and researchers to easily implement and experiment new approaches for automatic test-data generation. We show that Ocelot achieves a higher coverage compared to a competitive tool in 81% of the cases. Ocelot is publicly available to support both researchers and practitioners.","PeriodicalId":6622,"journal":{"name":"2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"46 1","pages":"868-871"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88598339","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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