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12th International Symposium on search-based software Engineering (SSBSE)

作者： Nejati, Shiva Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada Univ Luxembourg SnT Ctr Esch Sur Alzette Luxembourg

ISBN: (纸本)9783030597610;9783030597627

"testing can be quite effective for showing the presence of bugs, but is hopelessly inadequate for showing their absence". This famous remark, which was made by Dijkstra, has often been used to indicate a dichotomy between testing and verification. From a practitioner's point of view, however, there is not much difference in the ways testing and verification techniques may be used in practice. While engineers would try to demonstrate that their systems are correct (verification), they often find themselves in a situation where they have to prioritize bug finding (testing). As a result, the choice to go for formal verification versus testing is largely driven by the practical needs and the context specificities. In this keynote, I will focus on search-based software testing (SBST) and review some recent research that combines ideas from the SBST and the formal verification communities to improve the analysis of models of cyber physical systems (CPS). I will present an empirical study that compares CPS model testing and verification, a search-based testing approach for compute-intensive CPS models that builds on a well-known formal verification framework, and a technique to automatically generate formal environment assumptions for CPS models using search algorithms and genetic programming.

关键词： search-based software testing Formal verification Model testing Model checking Cyber physical systems

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Transferring interactive search-based software testing to industry

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JOURNAL OF SYSTEMS AND software 2018年 142卷 156-170页

作者： Marculescu, Bogdan Feldt, Robert Torkar, Richard Poulding, Simon Blekinge Inst Technol Karlskrona Sweden Chalmers Gothenburg Sweden Univ Gothenburg Gothenburg Sweden

Context: search-based software testing (SBST), and the wider area of search-based software Engineering (SBSE), is the application of optimization algorithms to problems in software testing, and software engineering, respectively. New algorithms, methods, and tools are being developed and validated on benchmark problems. In previous work, we have also implemented and evaluated Interactive search-based software testing (ISBST) tool prototypes, with a goal to successfully transfer the technique to industry. Objective: While SBST and SBSE solutions are often validated on benchmark problems, there is a need to validate them in an operational setting, and to assess their performance in practice. The present paper discusses the development and deployment of SBST tools for use in industry, and reflects on the transfer of these techniques to industry. Method: In addition to previous work discussing the development and validation of an ISBST prototype, a new version of the prototype ISBST system was evaluated in the laboratory and in industry. This evaluation is based on an industrial System under Test (SUT) and was carried out with industrial practitioners. The Technology Transfer Model is used as a framework to describe the progression of the development and evaluation of the ISBST system, as it progresses through the first five of its seven steps. Results: The paper presents a synthesis of previous work developing and evaluating the ISBST prototype, as well as presenting an evaluation, in both academia and industry, of that prototype's latest version. In addition to the evaluation, the paper also discusses the lessons learned from this transfer. Conclusions: This paper presents an overview of the development and deployment of the ISBST system in an industrial setting, using the framework of the Technology Transfer Model. We conclude that the ISBST system is capable of evolving useful test cases for that setting, though improvements in the means the system uses to communica

关键词： search-based software testing Interactive search-based software testing Industrial evaluation

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On the Impact of Lower Recall and Precision in Defect Prediction for Guiding search-based software testing

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ACM TRANSACTIONS ON software ENGINEERING AND METHODOLOGY 2024年第6期33卷 1-27页

作者： Perera, Anjana Turhan, Burak Aleti, Aldeida Boehme, Marcel Monash Univ Fac Informat Technol Wellington Rd Melbourne Vic 3800 Australia Oracle Labs Brisbane Qld Australia Univ Oulu Fac Informat Technol & Elect Engn Pentti Kaiteran Katu 1POB 3000 Oulu 90570 Finland Monash Univ Melbourne Vic Australia Max Planck Inst Secur & Privacy Univ Str 140 D-44799 Bochum Germany

Defect predictors, static bug detectors, and humans inspecting the code can propose locations in the program that are more likely to be buggy before they are discovered through testing. Automated test generators such as search-based software testing (SBST) techniques can use this information to direct their search for test cases to likely buggy code, thus speeding up the process of detecting existing bugs in those locations. Often the predictions given by these tools or humans are imprecise, which can misguide the SBST technique and may deteriorate its performance. In this article, we study the impact of imprecision in defect prediction on the bug detection effectiveness of SBST. Our study finds that the recall of the defect predictor, i.e., the proportion of correctly identified buggy code, has a significant impact on bug detection effectiveness of SBST with a large effect size. More precisely, the SBST technique detects 7.5 fewer bugs on average (out of 420 bugs) for every 5% decrements of the recall. However, the effect of precision, a measure for false alarms, is not of meaningful practical significance, as indicated by a very small effect size. In the context of combining defect prediction and SBST, our recommendation is to increase the recall of defect predictors as a primary objective and precision as a secondary objective. In our experiments, we find that 75% precision is as good as 100% precision. To account for the imprecision of defect predictors, in particular low recall values, SBST techniques should be designed to search for test cases that also cover the predicted non-buggy parts of the program, while prioritising the parts that have been predicted as buggy.

关键词： search-based software testing automated test generation defect prediction

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A systematic mapping addressing Hyper-Heuristics within search-based software testing

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INFORMATION AND software TECHNOLOGY 2019年第Oct.期114卷 176-189页

作者： Balera, Juliana Marino de Santiago Junior, Valdivino Alexandre INPE Lab Associado Computacao & Matemat Aplicada LABAC Av Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil Univ Nottingham Sch Comp Sci Jubilee CampusWollaton Rd Nottingham NG8 1BB England

Context: search-based software testing (SBST) is a research field where testing a software product is formulated as an optimization problem. It is an active sub-area of search-based software Engineering (SBSE) where many studies have been published and some reviews have been carried out. The majority of studies in SBST has been adopted meta-heuristics while hyper-heuristics have a long way to go. Moreover, there is still a lack of studies to perceive the state-of-the-art of the use of hyper-heuristics within SBST. Objective: The objective of this work is to investigate the adoption of hyper-heuristics for software testing highlighting the current efforts and identifying new research directions. Method: A Systematic mapping study was carried out with 5 research questions considering papers published up to may/2019, and 4 different bases. The research questions aims to find out, among other things, what are the hyper-heuristics used in the context of software testing, for what problems hyper-heuristics have been applied, and what are the objective functions in the scope of software testing. Results: A total of 734 studies were found via the search strings and 164 articles were related to software testing. However, from these, only 26 papers were actually in accordance with the scope of this research and 3 more papers were considered due to snowballing or expert's suggestion, totalizing 29 selected papers. Few different problems and application domains where hyper-heuristics have been considered were identified. Conclusion: Differently from other communities (Operational Research, Artificial Intelligence), SBST has little explored the benefits of hyper-heuristics which include generalization and less difficulty in parameterization. Hence, it is important to further investigate this area in order to alleviate the effort of practitioners to use such an approach in their testing activities.

关键词： search-based software testing Hyper-heuristics Systematic Mapping Evolutionary Algorithms Genetic Algorithms Meta-heuristics

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An Experimental Assessment of Using Theoretical Defect Predictors to Guide search-based software testing

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IEEE TRANSACTIONS ON software ENGINEERING 2023年第1期49卷 131-146页

作者： Perera, Anjana Aleti, Aldeida Turhan, Burak Bohme, Marcel Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Univ Oulu Fac Informat Technol & Elect Engn Oulu 90014 Finland

Automated test generators, such as search-based software testing (SBST) techniques are primarily guided by coverage information. As a result, they are very effective at achieving high code coverage. However, is high code coverage alone sufficient to detect bugs effectively? In this paper, we propose a new SBST technique, predictive many objective sorting algorithm (PreMOSA), which augments coverage information with defect prediction information to decide where to increase the test coverage in the class under test (CUT). Through an experimental evaluation using 420 labelled bugs on the Defects4J benchmark and using theoretical defect predictors, we demonstrate the improved effectiveness and efficiency of PreMOSA in detecting bugs when using any acceptable defect predictor, i.e., a defect predictor with recall and precision & ge;75%, compared to the state-of-the-art dynamic many objective sorting algorithm (DynaMOSA). PreMOSA detects up to 8.3% more labelled bugs on average than DynaMOSA when given a time budget of 2 minutes for test generation per CUT.

关键词： Computer bugs Codes Test pattern generators Sorting search problems software testing Pareto optimization search-based software testing Automated Test Generation Defect Prediction

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It really does matter how you normalize the branch distance in search-based software testing

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software testing VERIFICATION & RELIABILITY 2013年第2期23卷 119-147页

作者： Arcuri, Andrea Simula Res Lab Lysaker Norway

The use of search algorithms for test data generation has seen many successful results. For structural criteria like branch coverage, heuristics have been designed to help the search. The most common heuristic is the use of approach level (usually represented with an integer) to reward test cases whose executions get close (in the control flow graph) to the target branch. To solve the constraints of the predicates in the control flow graph, the branch distance is commonly employed. These two measures are linearly combined. Since the approach level is more important, the branch distance is normalized, often in the range [0, 1]. In this paper, different types of normalizing functions are analyzed. The analyses show that the one that is usually employed in the literature has several flaws. The paper presents a different normalizing function that is very simple and does not suffer from these limitations. Empirical and analytical analyses are carried out to compare these two functions. In particular, their effect is studied on commonly used search algorithms, such as Hill Climbing, Simulated Annealing and Genetic Algorithms. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： branch distance search-based software testing theory Simulated Annealing Genetic Algorithms test data generation

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Tester interactivity makes a difference in search-based software testing: A controlled experiment

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INFORMATION AND software TECHNOLOGY 2016年 78卷 66-82页

作者： Marculescu, Bogdan Poulding, Simon Feldt, Robert Petersen, Kai Torkar, Richard Blekinge Inst Technol Karlskrona Sweden Chalmers Gothenburg Sweden Univ Gothenburg Gothenburg Sweden

Context: search-based software testing promises to provide users with the ability to generate high quality test cases, and hence increase product quality, with a minimal increase in the time and effort required. The development of the Interactive search-based software testing (ISBST) system was motivated by a previous study to investigate the application of search-based software testing (SBST) in an industrial setting. ISBST allows users to interact with the underlying SBST system, guiding the search and assessing the results. An industrial evaluation indicated that the ISBST system could find test cases that are not created by testers employing manual techniques. The validity of the evaluation was threatened, however, by the low number of participants. Objective: This paper presents a follow-up study, to provide a more rigorous evaluation of the ISBST system. Method: To assess the ISBST system a two-way crossover controlled experiment was conducted with 58 students taking a Verification and Validation course. The NASA Task Load Index (NASA-TLX) is used to assess the workload experienced by the participants in the experiment. Results:The experimental results validated the hypothesis that the ISBST system generates test cases that are not found by the same participants employing manual testing techniques. A follow-up laboratory experiment also investigates the importance of interaction in obtaining the results. In addition to this main result, the subjective workload was assessed for each participant by means of the NASA-TLX tool. The evaluation showed that, while the ISBST system required more effort from the participants, they achieved the same performance. Conclusions: The paper provides evidence that the ISBST system develops test cases that are not found by manual techniques, and that interaction plays an important role in achieving that result. (C) 2016 Elsevier B.V. All rights reserved.

关键词： search-based software testing Interactive search-based software testing Controlled experiment

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An extensive evaluation of search-based software testing: a review

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SOFT COMPUTING 2019年第6期23卷 1933-1946页

作者： Khari, Manju Kumar, Prabhat Natl Inst Technol Dept Comp Engn Patna Bihar India

In recent years, search-based software testing (SBST) is the active research topic in software testing. SBST is the process of generating test cases that use metaheuristics for optimization of a task in the framework of software testing to solve difficult NP-hard problems. The best fitness results must be found with the heuristic search among many possibilities for a more cost-effective testing process and automate the process of generating test cases. Although search-based test data generation is a field of interest, some challenges remain unknown. The main objective of this survey is to find the main topics and trends in this emerging field of search-based software testing by examining the methods and the literature of software testing. A review of earlier studies of search-based software testing from the year 1996 to 2016 is discussed with the application of metaheuristics for the optimization of software testing.

关键词： search-based software testing Automated software test data generation Evolutionary testing Metaheuristic search Evolutionary algorithms Simulated annealing

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Broadening the search in search-based software testing: It Need Not Be Evolutionary 8

Broadening the Search in Search-Based Software Testing: It N...

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Eighth International Workshop on search-based software testing SBST

作者： Feldt, Robert Poulding, Simon Belkinge Inst Technol Dept Software Engn Karlskrona Sweden

ISBN: (纸本)9781467370790

search-based software testing (SBST) can potentially help software practitioners create better test suites using less time and resources by employing powerful methods for search and optimization. However, research on SBST has typically focused on only a few search approaches and basic techniques. A majority of publications in recent years use some form of evolutionary search, typically a genetic algorithm, or, alternatively, some other optimization algorithm inspired from nature. This paper argues that SBST researchers and practitioners should not restrict themselves to a limited choice of search algorithms or approaches to optimization. To support our argument we empirically investigate three alternatives and compare them to the de facto SBST standards in regards to performance, resource efficiency and robustness on different test data generation problems: classic algorithms from the optimization literature, bayesian optimization with gaussian processes from machine learning, and nested monte carlo search from game playing / reinforcement learning. In all cases we show comparable and sometimes better performance than the current state-of-the-SBST-art. We conclude that SBST researchers should consider a more general set of solution approaches, more consider combinations and hybrid solutions and look to other areas for how to develop the field.

关键词： Machine learning Operations research Reinforcement learning search-based software testing Operations Research program testing learning (artificial intelligence) Evolutionary Learning Artificial Intelligence Machine Learning Gaussian processes searchING ALGORITHM

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SBSTFrame: a Framework to search-based software testing

SBSTFrame: a Framework to Search-Based Software Testing

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Machado, Bruno N. Camilo-Junior, Celso G. Rodrigues, Cassio L. Quijano, Eduardo H. D. Univ Fed Goias Inst Informat Goiania Go Brazil

ISBN: (纸本)9781509018970

The software testing is an important component of software development life cycle, that directly affects quality of software products. Some problems in software testing phase can't be optimized only with traditional software Engineering techniques. It is possible to do the mathematical modelling of those problems in an attempt to optimize them through the search techniques. This study presents a framework for search-based software testing (SBST). The proposed framework works as a top-level layer over generic optimization frameworks and testing software tools, it's target is supporting software testers that are not able to use optimization frameworks during a testing activity due to short deadlines and limited resources or skills, also supporting expert or beginners users from optimization area that need or want to compare their metaheuristics with ones from literature and offered by the proposed framework. The framework was evaluated in a experiment of software testing scenario. This scenario was modeled as test case selection problem in which, experiments were executed with different metaheuristics. The results indicate it's capability to support the SBST area. The framework was evaluated and compared with other SBST framework in terms of quality metrics, that indicated it's extensibility and flexibility as framework.

关键词： search-based software testing framework metaheuristics software testing test case selection

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