“A Software Reliability Model Incorporating Fault Removal Efficiency and Its Release Policy”, a paper authored by Umashankar Samal and Ajay Kumar, and published in the Computational Statistics journal (9th November, 2023), proposes a new software reliability growth model (SRGM). This model is unique, the authors claim, in its consideration of fault removal efficiency, which is especially relevant in contemporary software development environments that extensively use automated testing and debugging tools.
SRGMs are mathematical models that are used to predict the improvement of software reliability over time, typically by analyzing the rate at which software faults are detected and corrected during the testing phase. These models help in planning, managing, and ensuring the quality of software development processes by providing insights into the likelihood of software failures.
Fault removal efficiency in software engineering is a measure of the effectiveness of the software testing and debugging process in identifying and eliminating defects from the software. It quantifies the ratio of the actual number of faults removed to the number of faults detected, indicating how efficiently a development team is addressing and resolving identified issues.
The paper begins by highlighting the role of SRGMs in assessing and predicting software reliability improvement over time. Traditional SRGMs primarily use the non-homogeneous Poisson process (NHPP), a model which represents the occurrence of events over time. The proposed model in this paper differs from these existing models by incorporating fault removal efficiency as a key parameter.
Samal and Kumar conducted a comparative analysis of their model with other established NHPP models using two datasets from software testing, demonstrating the superior fit of their proposed model. This indicates the improved ability of the SRGM to model software reliability improvements over time, providing motivation for future research in this area.
Full article available here.
