CSR: SMALL: Robust Algorithms for an Open Source Software Reliability Tool

University Of Massachusetts, Dartmouth, North Dartmouth MA

Investigators

Abstract

The key to the success of all software is its reliability. This project will develop an open source software reliability tool that will allow software engineers to automatically apply software reliability models to help organizations ensure that software applications they develop can operate free of failures. Traditional algorithms are numerically unstable, meaning that they can fail if initial estimates are inaccurate. Failure of an algorithm renders it impossible to apply a software reliability model to make useful predictions such as the amount of additional time a software application should be tested in order to achieve a desired level of reliability. The goal of this research is to develop numerically stable algorithms that will succeed even if the initial estimates are inaccurate. Expectation maximization (EM) and expectation conditional maximization (ECM) algorithms will be developed for failure rate and nonhomogeneous Poisson process (NHPP) software reliability models. Traditional EM algorithms impose restrictive assumptions that limit their application to only the simplest models, while the potential of the ECM algorithm has not been fully explored. Therefore, this research challenge will remove the restrictions of existing EM algorithms and design efficient ECM algorithms for software reliability models. Implementations of these numerically stable EM and ECM algorithms will be incorporated into the open source tool to ensure that software reliability models can be applied successfully. The enhanced stability of the algorithms and the open source nature of the tool may promote widespread use of quantitative software reliability models, enabling companies and organizations to improve time to market or field a software product.