AI Article Synopsis
- * The model incorporates a concept called "eventual bug size" as a hidden variable, which helps determine the likelihood of finding bugs based on their size and the potential number of inputs that could interact with them.
- * The researchers validated their model through simulations and applied it to actual data from commercial software and ISRO's launch mission, demonstrating its potential uses beyond software testing, such as in fields like hydrocarbon exploration.
Article Abstract
Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets - one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11610320 | PMC |
| http://dx.doi.org/10.1080/02664763.2024.2352751 | DOI Listing |
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