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   Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the TargetsPanichella, Annibale  ; ; in IEEE Transactions on Software Engineering (2018), 44(2), 122-158 The test case generation is intrinsically a multi-objective problem, since the goal is covering multiple test targets (e.g., branches). Existing search-based approaches either consider one target at a ... [more ▼] The test case generation is intrinsically a multi-objective problem, since the goal is covering multiple test targets (e.g., branches). Existing search-based approaches either consider one target at a time or aggregate all targets into a single fitness function (whole-suite approach). Multi and many-objective optimisation algorithms (MOAs) have never been applied to this problem, because existing algorithms do not scale to the number of coverage objectives that are typically found in real-world software. In addition, the final goal for MOAs is to find alternative trade-off solutions in the objective space, while in test generation the interesting solutions are only those test cases covering one or more uncovered targets. In this paper, we present DynaMOSA (Dynamic Many-Objective Sorting Algorithm), a novel many-objective solver specifically designed to address the test case generation problem in the context of coverage testing. DynaMOSA extends our previous many-objective technique MOSA (Many-Objective Sorting Algorithm) with dynamic selection of the coverage targets based on the control dependency hierarchy. Such extension makes the approach more effective and efficient in case of limited search budget. We carried out an empirical study on 346 Java classes using three coverage criteria (i.e., statement, branch, and strong mutation coverage) to assess the performance of DynaMOSA with respect to the whole-suite approach (WS), its archive-based variant (WSA) and MOSA. The results show that DynaMOSA outperforms WSA in 28% of the classes for branch coverage (+8% more coverage on average) and in 27% of the classes for mutation coverage (+11% more killed mutants on average). It outperforms WS in 51% of the classes for statement coverage, leading to +11% more coverage on average. Moreover, DynaMOSA outperforms its predecessor MOSA for all the three coverage criteria in 19% of the classes with +8% more code coverage on average. [less ▲] Detailed reference viewed: 288 (17 UL)LIPS vs MOSA: a Replicated Empirical Study on Automated Test Case GenerationPanichella, Annibale ; ; in International Symposium on Search Based Software Engineering (SSBSE) 2017 (2017, September 09) Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The ... [more ▼] Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The proponents of LIPS, a novel single-target test generator, conducted a preliminary empirical study to compare their approach with MOSA, an alternative many-objective test generator. However, their empirical investigation suffers from several external and internal validity threats, does not consider complex programs with many branches and does not include any qualitative analysis to interpret the results. In this paper, we report the results of a replication of the original study designed to address its major limitations and threats to validity. The new findings draw a completely different picture on the pros and cons of single-target vs many-objective approaches to test case generation. [less ▲] Detailed reference viewed: 179 (8 UL) Do Automatically Generated Test Cases Make Debugging Easier? An Experimental Assessment of Debugging Effectiveness and Efficiency; ; et al in ACM Transactions on Software Engineering and Methodology (2015), 25(1), 51--538 Detailed reference viewed: 151 (22 UL)Interpolated N-Grams for Model Based Testing; ; Nguyen, Duy Cu in Interpolated N-Grams for Model Based Testing (2014, May 31) Models – in particular finite state machines models – provide an invaluable source of information for the derivation of effective test cases. However, models usually approximate part of the program ... [more ▼] Models – in particular finite state machines models – provide an invaluable source of information for the derivation of effective test cases. However, models usually approximate part of the program semantics and capture only some of the relevant dependencies and constraints. As a consequence, some of the test cases that are derived from models are in- feasible. In this paper, we propose a method, based on the computation of the N-gram statistics, to increase the likelihood of deriving feasible test cases from a model. Furthermore, the level of model coverage is also expected to increase because infeasible test cases do not contribute to coverage. While N-grams do improve existing test case derivation methods, they show limitations when the N-gram statistics is incomplete, which is expected to necessarily occur as N increases. Interpolated N-grams overcome such limitation and show the highest performance of all test case derivation methods compared in this work. [less ▲] Detailed reference viewed: 154 (24 UL)Automated Inference of Classifications and Dependencies for Combinatorial TestingNguyen, Duy Cu ; Scientific Conference (2013, November 14) Detailed reference viewed: 118 (3 UL)N-Gram Based Test Sequence Generation from Finite State Models; ; Nguyen, Duy Cu Scientific Conference (2013, November 10) Detailed reference viewed: 151 (2 UL)Automated Generation of State Abstraction Functions using Data Invariant InferenceNguyen, Duy Cu ; ; et alScientific Conference (2013, May 18) Detailed reference viewed: 145 (3 UL)Automated Detection of Discontinuities in Models Inferred from Execution TracesNguyen, Duy Cu ; ; Scientific Conference (2011, March) Detailed reference viewed: 112 (2 UL) |
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