On the Expected Number of Failures Detected by Subdomain Testing and Random Testing
IEEE Transactions on Software Engineering
Towards a conceptual framework of software run reliability modeling
Information Sciences—Informatics and Computer Science: An International Journal
Prioritizing Test Cases For Regression Testing
IEEE Transactions on Software Engineering
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
Wavelet density estimators over data streams
Proceedings of the 2005 ACM symposium on Applied computing
Enhancing software reliability estimates using modified adaptive testing
Information and Software Technology
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The strategy used for testing a software system should not be fixed, because as time goes on we may have a better understanding of the software under test. A solution to this problem is to introduce control theory into software testing. We can use adaptive testing where the testing strategy is adjusted on-line by using the data collected during testing. Since the use of software components in software development is increasing, it is now more important than ever to adopt a good strategy for testing software components. In this paper, we use an adaptive testing strategy for testing software components. This strategy (AT_RLSEc with c indicating components) applies a recursive least squares estimation (RLSE) method to estimate parameters such as failure detection rate. It is different from the genetic algorithm-based adaptive testing (AT_GA) where a genetic algorithm is used for parameter estimation. Experimental data from our case study suggest that the fault detection effectiveness of AT_RLSEc is better than that of AT_GA and random testing.