2018 IEEE/ACM 11th International Workshop on Search-Based Software Testing (SBST)

28-29 May 2018

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  • [Title page i]

    Publication Year: 2018, Page(s): 1
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  • [Title page iii]

    Publication Year: 2018, Page(s): 3
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  • Table of contents

    Publication Year: 2018, Page(s):5 - 6
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  • Message from the ICSE 2018 General Chair

    Publication Year: 2018, Page(s):7 - 9
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  • Message from the SBST 2018 Co-Chairs

    Publication Year: 2018, Page(s):10 - 11
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  • Committees for SBST 2018

    Publication Year: 2018, Page(s):12 - 13
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  • ICSE 2018 Sponsors and Supporters

    Publication Year: 2018, Page(s): 14
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  • Predictive Analytics for Software Testing

    Publication Year: 2018, Page(s): 1
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (74 KB) | HTML iconHTML

    This keynote discusses the use of Predictive Analytics for Software Engineering, and in particular for Software Defect Prediction and Software Testing, by presenting the latest results achieved in these fields leveraging Artificial Intelligence, Search-based and Machine Learning methods, and by giving some directions for future work. View full abstract»

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  • Multifaceted Test Suite Generation Using Primary and Supporting Fitness Functions

    Publication Year: 2018, Page(s):2 - 5
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (110 KB) | HTML iconHTML

    Dozens of criteria have been proposed to judge testing adequacy. Such criteria are important, as they guide automated generation ef- forts. Yet, the current use of such criteria in automated generation contrasts how such criteria are used by humans. For a human, coverage is part of a multifaceted combination of testing strategies. In automated generation, coverage is typically the goal, and a sing... View full abstract»

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  • Search-Based Optimization for the Testing Resource Allocation Problem: Research Trends and Opportunities

    Publication Year: 2018, Page(s):6 - 12
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (308 KB) | HTML iconHTML

    This paper explores the usage of search-based techniques for the Testing Resource Allocation Problem (TRAP). We focus on the analysis of the literature, surveying the research proposals where search-based techniques are exploited for different formulations of the TRAP. Three dimensions are considered: the model formulation, solution, and validation. The analysis allows to derive several observatio... View full abstract»

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  • An Effective Approach for Regression Test Case Selection Using Pareto Based Multi-Objective Harmony Search

    Publication Year: 2018, Page(s):13 - 20
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (288 KB) | HTML iconHTML

    Regression testing is a way of catching bugs in new builds and releases to avoid the product risks. Corrective, progressive, retest all and selective regression testing are strategies to perform regression testing. Retesting all existing test cases is one of the most reliable approaches but it is costly in terms of time and effort. This limitation opened a scope to optimize regression testing cost... View full abstract»

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  • Evaluating Search-Based Techniques with Statistical Tests

    Publication Year: 2018, Page(s): 21
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (71 KB) | HTML iconHTML

    This tutorial covers the basics of how to use statistical tests to evaluate and compare search-algorithms, in particular when applied on software engineering problems. Search-algorithms like Hill Climbing and Genetic Algorithms are randomised. Running such randomised algorithms twice on the same problem can give different results. It is hence important to run such algorithms multiple times to coll... View full abstract»

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  • Java Unit Testing Tool Competition - Sixth Round

    Publication Year: 2018, Page(s):22 - 29
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1723 KB) | HTML iconHTML

    We report on the advances in this sixth edition of the JUnit tool competitions. This year the contest introduces new benchmarks to assess the performance of JUnit testing tools on different types of real-world software projects. Following on the statistical analyses from the past contest work, we have extended it with the combined tools performance aiming to beat the human made tests. Overall, the... View full abstract»

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  • T3 @SBST2018 Benchmark, and How Much We Can Get from Asemantical Testing

    Publication Year: 2018, Page(s):30 - 33
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (784 KB) | HTML iconHTML

    This paper discusses the performance of the automated testing tool for Java called T3 and compares it with few other tools and human written tests in a benchmark set by the Java Unit Testing Tool Contest 2018. Since all the compared tools rely on randomisation when generating their test data, albeit to different degrees and with different heuristics, this paper also tries to give some insight on j... View full abstract»

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  • EvoSuite at the SBST 2018 Tool Competition

    Publication Year: 2018, Page(s):34 - 37
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (110 KB) | HTML iconHTML

    EvoSuite is a search-based tool that automatically generates executable unit tests for Java code (JUnit tests). This paper summarises the results and experiences of EvoSuite's participation at the sixth unit testing competition at SBST 2018, where EvoSuite achieved the highest overall score (687 points) for the fifth time in six editions of the competition. View full abstract»

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  • Testing and Continuous Integration at Scale: Limits, Costs, and Expectations

    Publication Year: 2018, Page(s): 38
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (314 KB) | HTML iconHTML

    Build and verification systems and processes have changes significant in the past decade and so did the corresponding development processes. In fact, build and verification systems are a lower bound of how fast a company can ship new features-the more time spent to compile and test code the worse the company's ability to compete on the market. In other words, verification is key but needs to be af... View full abstract»

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  • From Operational to Declarative Specifications Using a Genetic Algorithm

    Publication Year: 2018, Page(s):39 - 42
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (131 KB) | HTML iconHTML

    In specification-based test generation, sometimes having a formal specification is not sufficient, since the specification may be in a different formalism from that required by the generation approach being used. In this paper, we deal with this problem specifically in the context in which, while having a formal specification in the form of an operational invariant written in a sequential programm... View full abstract»

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  • To Call, or Not to Call: Contrasting Direct and Indirect Branch Coverage in Test Generation

    Publication Year: 2018, Page(s):43 - 50
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (161 KB) | HTML iconHTML

    While adequacy criteria offer an end-point for testing, they do not mandate how targets are covered. Branch Coverage may be attained through direct calls to methods, or through indirect calls between methods. Automated generation is biased towards the rapid gains offered by indirect coverage. Therefore, even with the same end-goal, humans and automation produce very different tests. Direct coverag... View full abstract»

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  • Generating Test Input with Deep Reinforcement Learning

    Publication Year: 2018, Page(s):51 - 58
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (268 KB) | HTML iconHTML

    Test data generation is a tedious and laborious process. Search-based Software Testing (SBST) automatically generates test data optimising structural test criteria using metaheuristic algorithms. In essence, metaheuristic algorithms are systematic trial-and-error based on the feedback of fitness function. This is similar to an agent of reinforcement learning which iteratively decides an action bas... View full abstract»

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  • An Empirical Analysis of the Mutation Operator for Run-Time Adaptive Testing in Self-Adaptive Systems

    Publication Year: 2018, Page(s):59 - 66
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (226 KB) | HTML iconHTML

    A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood... View full abstract»

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  • On the Effect of Object Redundancy Elimination in Randomly Testing Collection Classes

    Publication Year: 2018, Page(s):67 - 70
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (120 KB) | HTML iconHTML

    In this paper, we analyze the effect of reducing object redundancy in random testing, by comparing the Randoop random testing tool with a version of the tool that disregards tests that only produce objects that have been previously generated by other tests. As a side effect, this variant also identifies methods in the software under test that never participate in state changes, and uses these more... View full abstract»

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  • Author Index

    Publication Year: 2018, Page(s): 71
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