Optimum bug fixing rate and bug fixing time detection by software reliability modelling

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rama Rao Narvaneni ◽  
K. Suresh Babu
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Mathematical Optimization ◽  
Release Time ◽  
Software Project ◽  
Reliability Growth ◽  
Content Type ◽  
Bug Fixing ◽  
Software Reliability Growth

PurposeSoftware reliability growth models (SRGMs) are used to assess and predict reliability of a software system. Many of these models are effective in predicting future failures unless the software evolves.Design/methodology/approachThis objective of this paper is to identify the best path for rectifying the BFT (bug fixing time) and BFR (bug fixing rate). Moreover, the flexible software project has been examined while materializing the BFR. To enhance the BFR, the traceability of bug is lessened by the version tag virtue in every software deliverable component. The release time of software build is optimized with the utilization of mathematical optimization mechanisms like ‘software reliability growth’ and ‘non-homogeneous Poisson process methods.’FindingsIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.Originality/valueIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.

Key Issues in Software Reliability Growth Models

2020 ◽  
Vol 13 ◽  
Author(s):  
Md. Asraful Haque ◽  
Nesar Ahmad
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Poisson Model ◽  
Release Time ◽  
Reliability Growth ◽  
Affecting Factors ◽  
Mathematical Basis ◽  
Data Set ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

Background: Software Reliability Growth Models (SRGMs) are most widely used mathematical models to monitor, predict and assess the software reliability. They play an important role in industries to estimate the release time of a software product. Since 1970s, researchers have suggested a large number of SRGMs to forecast software reliability based on certain assumptions. They all have explained how the system reliability changes over time by analyzing failure data set throughout the testing process. However, none of the models is universally accepted and can be used for all kinds of software. Objective: The objective of this paper is to highlight the limitations of SRGMs and to suggest a novel approach towards the improvement. Method: We have presented the mathematical basis, parameters and assumptions of software reliability model and analyzed five popular models namely Jelinski-Moranda (J-M) Model, Goel Okumoto NHPP Model, Musa-Okumoto Log Poisson Model, Gompertz Model and Enhanced NHPP Model. Conclusion: The paper focuses on the many challenges like flexibility issues, assumptions, and uncertainty factors of using SRGMs. It emphasizes considering all affecting factors in reliability calculation. A possible approach has been mentioned at the end of the paper.

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

2021 ◽  
pp. 1748006X2110337
Author(s):  
Vishal Pradhan ◽  
Ajay Kumar ◽  
Joydip Dhar
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Time Lag ◽  
Reduction Factor ◽  
Release Time ◽  
Data Sets ◽  
Time Lags ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Software Reliability Growth

The fault reduction factor (FRF) is a significant parameter for controlling the software reliability growth. It is the ratio of net fault correction to the number of failures encountered. In literature, many factors affect the behaviour of FRF, namely fault dependency, debugging time-lag, human learning behaviour and imperfect debugging. Besides this, several distributions, for example, inflection S-shaped, Weibull and Exponentiated-Weibull, are used as FRF. However, these standard distributions are not flexible to describe the observed behaviour of FRFs. This paper proposes three different software reliability growth models (SRGMs), which incorporate a three-parameter generalized inflection S-shaped (GISS) distribution as FRF. To model realistic SRGMs, time lags between fault detection and fault correction processes are also incorporated. This study proposed two models for the single release, whereas the third model is designed for multi-release software. Moreover, the first model is in perfect debugging, while the rest of the two are in an imperfect debugging environment. The extensive experiments are conducted for the proposed models with six single release and one multi-release data-sets. The choice of GISS distribution as an FRF improves the software reliability evaluation in comparison with the existing systems in the literature. Finally, the development cost and optimal release time are calculated in a perfect debugging environment.

TESTING-DOMAIN DEPENDENT SOFTWARE RELIABILITY GROWTH MODELS AND THEIR COMPARISONS OF GOODNESS-OF-FIT

2001 ◽  
Vol 08 (03) ◽  
pp. 205-218 ◽  
Author(s):  
SHIGERU YAMADA ◽  
TAKAJI FUJIWARA
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Test Case ◽  
Test Cases ◽  
Software System ◽  
Reliability Growth ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

A software developer has to test to verify the implemented functions based on its requirement specification. We use many various test-cases for testing. Then, there is a set of the modules and functions in the software system to be influenced by the executed test-cases. The set is called a testing-domain and it spreads with the progress of testing. The growth rate of testing-domain in the software system is closely related to the quality and quantity of the executed test-cases by testing. Further, the quality of test-cases is related to the testing-skill of test-case designers. In this paper, we discuss testing-domain dependent software reliability growth models. The models are formulated by a nonhomogeneous Poisson process. Further, we propose three kinds of testing-domain, i.e., the basic testing-domain, the testing-domain with skill-factor, and the testing-domain with imperfect debugging. Finally, these models are applied to fault data observed in actual development projects, the software reliability analysis results are shown, and the comparisons of goodness-of-fit with the conventional software reliability growth models are performed.

DEBUGGING PROCESS-ORIENTED DISCRETE SOFTWARE RELIABILITY MODELING

2009 ◽  
Vol 16 (04) ◽  
pp. 357-370 ◽  
Author(s):  
SHINJI INOUE ◽  
NAOKI IWAMOTO ◽  
SHIGERU YAMADA
Keyword(s):  
Discrete Time ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Reliability Growth ◽  
Reliability Modeling ◽  
New Approach ◽  
Time Modeling ◽  
Software Reliability Growth ◽  
Discrete Time Modeling

This paper discusses an new approach for discrete-time software reliability growth modeling based on an discrete-time infinite server queueing model, which describes a debugging process in a testing phase. Our approach enables us to develop discrete-time software reliability growth models (SRGMs) which could not be developed under conventional discrete-time modeling approaches. This paper also discuss goodness-of-fit comparisons of our discrete-time SRGMs with conventional continuous-time SRGMs in terms of the criterion of the mean squared errors, and show numerical examples for software reliability analysis of our models by using actual data.

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