Effect of Test Coverage and Change Point on Software Reliability Growth Based on Time Variable Fault Detection Probability

Chatterjee, Saikat; Shukla, Ankur

doi:10.17706/jsw.11.1.110-117

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Here time-variable testing coverage functions are chosen to obtain the following specific models, and these testing coverage functions have been recommended in several references with great flexibility [41].…”

Section: Framework and New Testing Coverage Modelsmentioning

confidence: 99%

Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

Pham

2021

Mathematics

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This paper presents a general testing coverage software reliability modeling framework that covers imperfect debugging and considers not only fault detection processes (FDP) but also fault correction processes (FCP). Numerous software reliability growth models have evaluated the reliability of software over the last few decades, but most of them attached importance to modeling the fault detection process rather than modeling the fault correction process. Previous studies analyzed the time dependency between the fault detection and correction processes and modeled the fault correction process as a delayed detection process with a random or deterministic time delay. We study the quantitative dependency between dual processes from the viewpoint of fault amount dependency instead of time dependency, then propose a generalized modeling framework along with imperfect debugging and testing coverage. New models are derived by adopting different testing coverage functions. We compared the performance of these proposed models with existing models under the context of two kinds of failure data, one of which only includes observations of faults detected, and the other includes not only fault detection but also fault correction data. Different parameter estimation methods and performance comparison criteria are presented according to the characteristics of different kinds of datasets. No matter what kind of data, the comparison results reveal that the proposed models generally give improved descriptive and predictive performance than existing models.

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Section: Framework and New Testing Coverage Modelsmentioning

confidence: 99%

Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

Pham

2021

Mathematics

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“…In the past, various testing coverage functions based on time have been introduced like Rayleigh [40] , Weibull and logistic [13] , log-exponential [26] , s-shaped [34] , beta & hyper exponential [3] and lognormal [31] . Testing coverage is thought to be one of the valuable attributes to identify the effectiveness of the software ( [4] ; Subhashis [5] ; S [6] ). None of these SRGMs have considered the testing coverage with random effects.…”

Section: Introductionmentioning

confidence: 99%

Multi-release software model based on testing coverage incorporating random effect (SDE)

et al. 2023

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“…Moreover, some, but not all, studies use measures that contain the word predictive in their name even though they do not employ standard methods from statistics, which fit the model to a subset of the data and use the remaining data to assess the predictive capability of the model. Examples of past studies utilize mean square error , bias , variance , R‐squared , root mean square prediction error , predictive relative error , Kolmogorov–Smirnov test , the Theil statistic , sum of squared error and mean error of prediction , while Inoue et al. considered the Akaike information criterion (AIC) to compare the Jelinski–Moranda model with and without CP.…”

Section: Introductionmentioning

confidence: 99%

A heterogeneous single changepoint software reliability growth model framework

Nagaraju

Fiondella

Wandji

2019

Software Testing Verif & Rel

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Many non-homogeneous Poisson process software reliability growth models (SRGM) are characterized by a single continuous curve. However, failures are driven by factors such as the testing strategy and environment, integration testing and resource allocation, which can introduce one or more changepoint into the fault detection process. Some researchers have proposed non-homogeneous Poisson process SRGM, but only consider a common failure distribution before and after changepoints. This paper proposes a heterogeneous single changepoint framework for SRGM, which can exhibit different failure distributions before and after the changepoint. Combinations of two simple and distinct curves including an exponential and S-shaped curve are employed to illustrate the concept. Ten data sets are used to compare these heterogeneous models against their homogeneous counterparts. Experimental results indicate that heterogeneous changepoint models achieve better goodness-of-fit measures on 60% and 80% of the data sets with respect to the Akaike information criterion and predictive sum of squares measures.

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Effect of Test Coverage and Change Point on Software Reliability Growth Based on Time Variable Fault Detection Probability

Cited by 9 publications

References 21 publications

Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

Software Reliability Modeling Incorporating Fault Detection and Fault Correction Processes with Testing Coverage and Fault Amount Dependency

Multi-release software model based on testing coverage incorporating random effect (SDE)

A heterogeneous single changepoint software reliability growth model framework

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