Software reliability prediction and release time management with coverage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saurabh Panwar ◽  
Vivek Kumar ◽  
P.K. Kapur ◽  
Ompal Singh
Keyword(s):  
Fault Detection ◽  
Software Reliability ◽  
Release Time ◽  
Software System ◽  
Reliability Growth ◽  
Time To Market ◽  
Content Type ◽  
Detection Process ◽  
Growth Phenomenon ◽  
Testing Coverage

PurposeSoftware testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested.Design/methodology/approachThe fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables.FindingsThe practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches.Originality/valueThis study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testing duration.

A SOFTWARE RELIABILITY GROWTH MODEL WITH TESTING EFFORT DEPENDENT LEARNING FUNCTION FOR DISTRIBUTED SYSTEMS

2004 ◽  
Vol 11 (04) ◽  
pp. 365-377 ◽  
Author(s):  
P. K. KAPUR ◽  
D. N. GOSWAMI ◽  
AMIT GUPTA
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Software Process Improvement ◽  
Software System ◽  
Reliability Growth ◽  
Development Environment ◽  
Testing Effort ◽  
Growth Phenomenon ◽  
Software Reliability Growth ◽  
Fault Removal

Effective software process improvement will not start until management insists that product development work be planned and properly managed. This becomes even more challenging in an increasing number of major system developments made up from distributed sub-system software projects. These sub-systems are integrated and validated to provide the final system and product release. The need is growing to estimate, risk assess, plan and manage the development of these distributed sub-systems and the final full system release. In this paper, an attempt has been made to model the software reliability growth phenomenon with testing effort in a distributed development environment. Proposed Non Homogeneous Poisson Process (NHPP) based model assumes that the software system consists of a finite number of reused and newly developed sub-systems. The reused sub-systems do not consider the effect of severity of the faults on the software reliability growth phenomenon because they stabilize over a period of time i.e., the growth is uniform whereas, the newly developed sub-system do consider that. Fault removal phenomenon for reused and newly developed sub-systems have been modeled separately and is summed up to get the total fault removal phenomenon of the software system. The applicability of our model is shown by validating it on software failure data sets obtained from different real software development projects. The comparisons with established models in terms of goodness of fit, the Akaike Information Criterion (AIC), Mean of Squared Errors (MSE) have been presented.

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

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Qiuying Li ◽  
Hoang Pham
Keyword(s):  
Fault Detection ◽  
Software Reliability ◽  
Estimation Methods ◽  
Time Dependency ◽  
Modeling Framework ◽  
Reliability Modeling ◽  
Imperfect Debugging ◽  
Detection Process ◽  
Correction Process ◽  
Testing Coverage

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.

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.

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