By Claire Le Goues, Shin Yoo

This ebook constitutes the refereed lawsuits of the sixth overseas Symposium on Search-Based software program Engineering, SSBSE 2014, held in Fortaleza, Brazil.
The 14 revised complete papers provided including 2 keynote addresses, 1 invited speak, 1 brief paper, three papers of the graduate tune, and four problem song papers have been rigorously reviewed and chosen from fifty one submissions. seek dependent software program Engineering (SBSE) stories the applying of meta-heuristic optimization innovations to numerous software program engineering difficulties, starting from specifications engineering to software program checking out and maintenance.

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

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In: ACM/IEEE International Conference on Software Engineering (ICSE), pp. 178–188 (2012) 10. : Handling test length bloat. 1495 11. : Whole test suite generation. IEEE Transactions on Software Engineering 39(2), 276–291 (2013) 12. : Achieving scalable mutation-based generation of whole test suites. Empirical Software Engineering (EMSE) (to appear, 2014) 13. : Mutation-driven generation of unit tests and oracles. IEEE Transactions on Software Engineering (TSE) 28(2), 278–292 (2012) 14. : Optimizing for the number of tests generated in search based test data generation with an application to the oracle cost problem.

The details about the main aspects of our algorithm are presented in the following subsections. Solution Encoding. A solution is a decomposition of the execution trace into different chunks representing phases. Our algorithm searches for the best cut positions to decompose the execution trace into phases. These cut positions in the trace are events where a phase shift occurs. Figure 2 (left) schematizes two solutions: A and B. The rectangle represents the entire trace, that is divided in two phases in solution A with one cut position (dashed line), and in four phases in solution B with three cut positions.

Phase identification solution: A solution is a set of cut positions that decomposes the trace into phases. 2 Heuristics Our approach for phase identification stands on assumptions concerning the activities of objects during their lifetime. Our rationale is based on the role of objects during program execution and is outlined as follows: – Two successive execution phases should not have many active objects in common, otherwise it could suggest that the program is still within the same phase. – Not all objects active in a phase are representative of this phase.

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