Abstract:Software errors can be a serious problem, because of possible damages (and related costs) and the burden of the needed corrections. Software testing, whose aim is to discover the errors in software pr...Software errors can be a serious problem, because of possible damages (and related costs) and the burden of the needed corrections. Software testing, whose aim is to discover the errors in software products, requires a lot of resources and from it derives the overall quality (i.e. reliability) of the software product. Testing is a decisive factor in attempting to discover as soon as possible, and so reduce, the presence of errors; it involves a lot of resources against an error discovery expectation, and is thus susceptible of optimization: i.e., the best equilibrium between the number of tests to make and the global expected value of discovered errors has actually to be achieved. In fact, it is not economically feasible to proceed with testing over a given limit as well as to execute too few tests, running in the risk of having too heavy expenses because of too residual errors. In the present work we propose some models of software testing optimization, making use of an integer linear programming approach solved with a “branch & bound” algorithm.Read More
Publication Year: 2006
Publication Date: 2006-05-01
Language: en
Type: preprint
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot