By Claire Le Goues, Shin Yoo

This booklet constitutes the refereed complaints of the sixth foreign Symposium on Search-Based software program Engineering, SSBSE 2014, held in Fortaleza, Brazil.
The 14 revised complete papers offered including 2 keynote addresses, 1 invited speak, 1 brief paper, three papers of the graduate song, and four problem song papers have been conscientiously reviewed and chosen from fifty one submissions. seek dependent software program Engineering (SBSE) reviews the appliance of meta-heuristic optimization recommendations to numerous software program engineering difficulties, starting from requisites engineering to software program checking out and maintenance.

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Extra info for Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings

Sample text

3 Detection Algorithm We approach phase identification as an optimization problem. We consider phases as subsets of the execution events contained in the execution trace. The phase detection problem then becomes one of determining the best decomposition of the execution trace’s events. Considering that an execution trace contains n events (possibly in the order of hundreds of thousands), and that a particular execution contains any number m of phases, the number of possible solutions is Ckl , where 0 ≤ l ≤ (n/2) − 2, k = m−1 is the number phase shifts in the trace, and l is the number of positions Detecting Program Execution Phases Using Heuristic Search 19 in the execution trace where a phase shift can occur.

There are different ways to translate the proposed heuristics into measurable properties that allow evaluating the quality of a phase detection solution. We propose three metrics that are combined to define fitness functions for our problem. 1) Phase Coupling: Two successive phases are coupled if they share objects. An object is shared by phases if its lifetime covers them. Figure 1 illustrates eight different cases that can occur when computing coupling over two phases. These cases differ in the way object lifetimes are distributed over the two successive phases.

On the other hand, there are 1,402 cases (out of 2,383) in which Whole gives better results. For 246 of them, the OneBranch approach never managed to generate any results in any of the 1,000 runs. , three) difficult goals that only OneBranch can cover, there are many more (246/3 = 82 times) difficult branches that only Whole does cover. RQ2: Whole test suite generation is able to handle 82 times more difficult branches than OneBranch. Once assessed that the Whole approach leads to higher coverage, even for the difficult branches, it is important to study what are the conditions in which this improvement is obtained.

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