| 1. |
- Marculescu, Bogdan, 1984, et al.
(författare)
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Objective re-weighting to guide an interactive search based software testing system
- 2013
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Ingår i: 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013; Miami, FL; United States; 4 December 2013 through 7 December 2013. - Miami : IEEE. ; 2, s. 102-107
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Konferensbidrag (refereegranskat)abstract
- Even hardware-focused industries today develop products where software is both a large and important component. Engineers tasked with developing and integrating these products do not always have a software engineering background. To ensure quality, tools are needed that automate and support software testing while allowing these domain specialists to leverage their knowledge and experience. Search-based testing could be a key aspect in creating an automated tool for supporting testing activities. However, domain specific quality criteria and trade-offs make it difficult to develop a general fitness function a priori, so interaction between domain specialists and such a tool would be critical to its success. In this paper we present a system for interactive search based software testing and investigate a way for domain specialists to guide the search by dynamically re-weighting quality goals. Our empirical investigation shows that objective reweighing can help a human domain specialist interactively guide the search, without requiring specialised knowledge of the system and without sacrificing population diversity.
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| 2. |
- Marculescu, Bogdan, 1984, et al.
(författare)
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Using exploration focused techniques to augment search-based software testing: an experimental evaluation
- 2016
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Ingår i: 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST). - : IEEE. - 2381-2834. - 9781509018277 - 9780306406157 ; , s. 69-79
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Konferensbidrag (refereegranskat)abstract
- Search-based software testing (SBST)often uses objective-based approaches to solve testing problems. There are, however, situations where the validity and completeness of objectives cannot be ascertained, or where there is insufficient information to define objectives at all. Incomplete or incorrect objectives may steer the search away from interesting behavior of the software under test (SUT) and from potentially useful test cases.This papers investigates the degree to whichexploration-based algorithms can be used tocomplement an objective-based tool we have previously developed and evaluated in industry. In particular, we would like to assess how exploration-based algorithms perform in situations where little information on the behavior space is available a priori. We have conducted an experiment comparing the performance of an exploration-based algorithm with an objective-based one on a problem with a high dimensional behavior space. In addition, we evaluate to what extent that performance degrades in situations where computational resources are limited.Our experiment shows that exploration-basedalgorithms are useful in covering a larger area of the behavior space and result in a more diverse solution population. Typically, of the candidate solutions that exploration-based algorithms propose, more than 80% were not covered by their objective-based counterpart. This increased diversity is present in the resulting population even when computational resources are limited. We conclude that exploration-focused algorithms are a useful means of investigating high-dimensional spaces, even in situations where limited information and limited resources are available.
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