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Recommending Faulty Configurations for Interacting Systems Under Test Using multi-objective search

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2021年第4期30卷 1–36页

作者： Safdar, Safdar Aqeel Yue, Tao Ali, Shaukat Simula Res Lab Fornebu Norway Univ Oslo Oslo Norway Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Martin Linges Vei 25 N-1364 Oslo Norway

Modern systems, such as cyber-physical systems, often consist of multiple products within/across product lines communicating with each other through information networks. Consequently, their runtime behaviors are influenced by product configurations and networks. Such systems play a vital role in our daily life;thus, ensuring their correctness by thorough testing becomes essential. However, testing these systems is particularly challenging due to a large number of possible configurations and limited available resources. Therefore, it is important and practically useful to test these systems with specific configurations under which products will most likely fail to communicate with each other. Motivated by this, we present a search-based configuration recommendation (SBCR) approach to recommend faulty configurations for the system under test (SUT) based on cross-product line (CPL) rules. CPL rules are soft constraints, constraining product configurations while indicating the most probable system states with a certain degree of confidence. In SBCR, we defined four search objectives based on CPL rules and combined them with six commonly applied search algorithms. To evaluate SBCR (i.e., SBCRNSGA-II, SBCRIBEA, SBCRMoCell, SBCRSPEA2, SBCRPAES, and SBCRSMPSO), we performed two case studies (Cisco and Jitsi) and conducted difference analyses. Results show that for both of the case studies, SBCR significantly outperformed random search-based configuration recommendation (RBCR) for 86% of the total comparisons based on six quality indicators, and 100% of the total comparisons based on the percentage of faulty configurations (PFC). Among the six variants of SBCR, SBCRSPEA2 outperformed the others in 85% of the total comparisons based on six quality indicators and 100% of the total comparisons based on PFC.

关键词： Product line configuration recommendation testing mined rules multi-objective search interacting products

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Using multi-objective search and machine learning to infer rules constraining product configurations

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AUTOMATED SOFTWARE ENGINEERING 2020年第1-2期27卷 1-62页

作者： Safdar, Safdar Aqeel Yue, Tao Ali, Shaukat Lu, Hong Simula Res Lab Oslo Norway Univ Oslo Oslo Norway

Modern systems are being developed by integrating multiple products within/across product lines that communicate with each other through information networks. Runtime behaviors of such systems are related to product configurations and information networks. Cost-effectively supporting Product Line Engineering (PLE) of such systems is challenging mainly because of lacking the support of automation of the configuration process. Capturing rules is the key for automating the configuration process in PLE. However, there does not exist explicitly-specified rules constraining configurable parameter values of such products and product lines. Manually specifying such rules is tedious and time-consuming. To address this challenge, in this paper, we present an improved version (named as SBRM+) of our previously proposed search-based Rule Mining (SBRM) approach. SBRM+ incorporates two machine learning algorithms (i.e., C4.5 and PART) and two multi-objective search algorithms (i.e., NSGA-II and NSGA-III), employs a clustering algorithm (i.e., k means) for classifying rules as high or low confidence rules, which are used for defining three objectives to guide the search. To evaluate SBRM+ (i.e., SBRMNSGA-II+-C45, SBRMNSGA-III+\-C45, SBRMNSGA-II+-PART, and SBRMNSGA-III+-PART), we performed two case studies (Cisco and Jitsi) and conducted three types of analyses of results: difference analysis, correlation analysis, and trend analysis. Results of the analyses show that all the SBRM+ approaches performed significantly better than two Random search-based approaches (RBRM+-C45 and RBRM+-PART) in terms of fitness values, six quality indicators, and 17 machine learning quality measurements (MLQMs). As compared to RBRM+ approaches, SBRM+ approaches have improved the quality of rules based on MLQMs up to 27% for the Cisco case study and 28% for the Jitsi case study.

关键词： Product line Configuration Rule mining multi-objective search Machine learning Interacting products

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Mining Cross Product Line Rules with multi-objective search and Machine Learning 17

Mining Cross Product Line Rules with Multi-Objective Search ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Safdar, Safdar Aqeel Lu, Hong Yue, Tao Ali, Shaukat Simula Res Lab Oslo Norway Univ Oslo Oslo Norway

ISBN: (纸本)9781450349208

Nowadays, an increasing number of systems are being developed by integrating products (belonging to different product lines) that communicate with each other through information networks. Cost-effectively supporting Product Line Engineering (PLE) and in particular enabling automation of configuration in PLE is a challenge. Capturing rules is the key for enabling automation of configuration. Product configuration has a direct impact on runtime interactions of communicating products. Such products might be within or across product lines and there usually don't exist explicitly specified rules constraining configurable parameter values of such products. Manually specifying such rules is tedious, time-consuming, and requires expert's knowledge of the domain and the product lines. To address this challenge, we propose an approach named as SBRM that combines multi-objective search with machine learning to mine rules. To evaluate the proposed approach, we performed a real case study of two communicating Video Conferencing Systems belonging to two different product lines. Results show that SBRM performed significantly better than Random search in terms of fitness values, Hyper-Volume, and machine learning quality measurements. When comparing with rules mined with real data, SBRM performed significantly better in terms of Failed Precision (18%), Failed Recall (72%), and Failed F-measure (59%).

关键词： Rule Mining multi-objective search Configuration Machine Learning Product Line

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Reducing Interactive Refactoring Effort via Clustering-Based multi-objective search 18

Reducing Interactive Refactoring Effort via Clustering-Based...

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33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Alizadeh, Vahid Kessentini, Marouane Univ Michigan CIS Dept Dearborn MI 48128 USA

ISBN: (纸本)9781450359375

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 errorprone, 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.

关键词： search based software engineering refactoring multi-objective search clustering

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Method-level bug localization using hybrid multi-objective search

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INFORMATION AND SOFTWARE TECHNOLOGY 2021年 131卷

作者： Almhana, Rafi Kessentini, Marouane Mkaouer, Wiem Univ Michigan Dept Comp & Informat Sci Ann Arbor MI 48109 USA Rochester Inst Technol Dept Software Engn Rochester NY 14623 USA

Context: One of the time-consuming maintenance tasks is the localization of bugs especially in large software systems. Developers have to follow a tedious process to reproduce the abnormal behavior then inspect a large number of files. While several studies have been proposed for bugs localization, the majority of them are recommending classes/files as outputs which may still require high inspection effort. Furthermore, there is a significant difference between the natural language used in bug reports and the programming language which limits the efficiency of existing approaches since most of them are mainly based on lexical similarity. : In this paper, we propose an automated approach to find and rank the potential methods in order to localize the source of a bug based on a bug report description. Method: Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a hybrid multi-objective optimization algorithm combining local and global search. The relevance of the recommended code fragments is estimated based on the use of the history of changes and bug-fixing, and the lexical similarity between the bug report description and the API documentation. Our approach operates on two main steps. The first step is to find the best set of classes satisfying the two conflicting criteria of relevance and the number of classes to recommend using a global search based on NSGA-II. The second step is to locate the most appropriate methods to inspect, using a local multi objective search based on Simulated Annealing (MOSA) from the list of classes recommended by the first step. Results: We evaluated our system on 6 open source Java projects, using the version of the project before fixing the bug of many bug reports. Our hybrid multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 78% of the bug reports leading to a significan

关键词： Bugs localization Fault localization search-based software engineering Bug reports multi-objective search Simulated annealing Software maintenance

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Third-party software library migration at the method-level using multi-objective evolutionary search

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 84卷

作者： Deshpande, Niranjana Mkaouer, Mohamed Wiem Ouni, Ali Sharma, Naveen Rochester Inst Technol Rochester NY 14623 USA Univ Quebec ETS Montreal Montreal PQ Canada

Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open -source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, a

关键词： search-based software engineering Library migration API migration NSGAII multi-objective search

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An Adaptive multi-objective Heuristic search for Model-Based Testing 11

An Adaptive Multi-Objective Heuristic Search for Model-Based...

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11th International Conference on the Quality of Information and Communications Technology (QUATIC)

作者： de Almeida Neto, Aristides Martins, Eliane State Univ Campinas UNICAMP Inst Comp Campinas SP Brazil

ISBN: (纸本)9781538658413

search-based Testing (SBST) consists in the application of metaheuristic search techniques for test case generation. Most studies are focused on the generation of test data for white-box testing, but there are also proposals for model-based test case generation. In previous works, our group developed a method and a tool that supports multi-objective metaheuristic search techniques for test case generation from state models. A problem that practitioners and researchers face when employing search-based test case generation tools is that the algorithms must be properly parameterized for the different problems and even problem instances. However, proper parameter selection is a highly specialized and time-consuming task, which most often is not part of practitioner's skills. In this paper, we introduce an adaptive approach for test case generation in the context of model-based testing. After a literature review, to understand the current approaches for parameter adjustment, we selected some of them to implement. The different algorithms were compared with manual parameter tuning. Preliminary results indicate that the different parameter control algorithms used in this study can be useful and were integrated to the test case generation tool. Their performance may vary according to the model at hand, that is why we think it is better to offer distinct options to the users, besides the already existing manual parameter setting.

关键词： search-based testing adaptive parameter control multi-objective search

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X-SBR: On the Use of the History of Refactorings for Explainable search-Based Refactoring and Intelligent Change Operators

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第10期48卷 3753-3770页

作者： Abid, Chaima Rzig, Dhia Ferreira, Thiago Kessentini, Marouane Sharma, Tushar Univ Michigan Dept Comp & Informat Sci Dearborn MI 48128 USA Siemens Corp Technol Charlotte NC 28227 USA

Refactoring is widely adopted nowadays in industry to restructure the code and meet high quality while preserving the external behavior. Many of the existing refactoring tools and research are based on search-based techniques to find relevant recommendations by finding trade-offs between different quality attributes. While these techniques show promising results on open-source and industry projects, they lack explanations of the recommended changes which can impact their trustworthiness when adopted in practice by developers. Furthermore, most of the adopted search-based techniques are based on random population generation and random change operators (e.g., crossover and mutation). However, it is critical to understand which good refactoring patterns may exist when applying change operators to either keep them or exchange with other solutions rather than destroying them with random changes. In this paper, we propose knowledge-informed change operators and an improved seeding mechanism that we integrated in a multi-objective genetic algorithm. We also provide explanations for refactoring solutions. First, we generate association rules using the Apriori algorithm to find relationships between applied refactorings in previous commits, their locations, and their rationale (quality improvements). Then, we use these rules to 1) initialize the population, 2) improve the change operators and seeding mechanisms of the multi-objective search in order to preserve and exchange good patterns in the refactoring solutions, and 3) explain how a sequence of refactorings collaborate in order to improve the quality of the system (e.g., fitness functions). The validation on large open-source systems shows that X-SBR provides refactoring solutions of a better quality than those given by the state-of-the-art techniques in terms of reducing the invalid refactorings, improving the quality, and increasing trustworthiness of the developers in the suggested refactorings via the provided expla

关键词： Measurement Statistics Sociology Tools History Software systems Software engineering Refactoring recommendations search-based software engineering QMOOD metrics multi-objective search

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search-Based Selection and Prioritization of Test Scenarios for Autonomous Driving Systems 13th

Search-Based Selection and Prioritization of Test Scenarios ...

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13th Symposium on search-Based Software Engineering (SSBSE)

作者： Lu, Chengjie Zhang, Huihui Yue, Tao Ali, Shaukat Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Weifang Univ Weifang Peoples R China Simula Res Lab Oslo Norway

ISBN: (纸本)9783030881061;9783030881054

Violating the safety of autonomous driving systems (ADSs) could lead to fatal accidents. ADSs are complex, constantly-evolving and software-intensive systems. Testing an individual ADS is challenging and expensive on its own, and consequently testing its multiple versions (due to evolution) becomes much more costly. Thus, it is needed to develop approaches for selecting and prioritizing tests for newer versions of ADSs based on historical test execution data of their previous versions. To this end, we propose a multi-objective search-based approach for Selection and Prioritization of tEst sCenarios for auTonomous dRiving systEms (SPECTRE) to test newer versions of an ADS based on four optimization objectives, e.g., demand of a test scenario put on an ADS. We experimented with five commonly used multi-objective evolutionary algorithms and used a repository of 60,000 test scenarios. Among all the algorithms, IBEA achieved the best performance for solving all the optimization problems of varying complexity.

关键词： Test optimization multi-objective search Autonomous driving

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Personalized Web Services Interface Design Using Interactive Computational search

Personalized Web Services Interface Design Using Interactive...

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作者： FNU Jirigesi University of Michigan Dearborn

学位级别：硕士

Most of successful Web services evolve through a process of continuous change due to several reasons such as improving the quality, fixing bugs and adding new features. However, this evolution process may weaken the design of the Web service's interface by including a large number of non-cohesive operations and make it unnecessarily complex for users to find relevant operations to be used by their services-based systems. In this thesis, we propose a remodularization recommendation approach that dynamically adapts and interactively suggests a possible modularization of the Web services interface design to users/developers and takes their feedback into consideration. Our approach uses an interactive multi-criteria decision making algorithm, based on interactive NSGA-II, to find a set of good design interface modularization solutions that find a trade-off between improving several interface design quality metrics (e. g. coupling, cohesion, number of portTypes and number of antipatterns), maximizing the reuse of user-interface interaction history patterns identified from previous releases and satisfying the interaction constraints learnt from the user feedback during the execution of the algorithm while minimizing the deviation from the initial design. We evaluated our approach on a set of 22 real-world Web services, provided by Amazon and Yahoo. Statistical analysis of our experiments shows that our dynamic interactive Web services interface modularization approach performed significantly better than the state-of-the-art modularization techniques.

关键词： Web services interface design quality multi-objective search

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