On Adaptive Sampling-Based Testing for Software Reliability Assessment

Assessing reliability of software programs during validation is a challenging task for engineers. The assessment is not only required to be unbiased, but it needs to provide tight variance (hence, tight confidence interval) with as few test cases as possible. Statistical sampling is a theoretically sound approach for reliability testing, but it is often impractical in its current form, because of too many test cases required to achieve desired confidence levels, especially when the software has few residual faults inside. We claim that the potential of statistical sampling methods is largely underestimated. This paper presents an adaptive sampling-based testing (AST) strategy for reliability assessment. A two-stage conceptual framework is defined, where adaptiveness is included to uncover residual faults earlier, while various sampling-based techniques are proposed to improve the efficiency (in terms of variance-test cases tradeoff) by better exploiting the information available to tester. An empirical study is conducted to assess the AST performance and compare the proposed sampling techniques to each other on real programs.