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Various Software Reliability Growth Models (SRGMs) have been developed over last five decades to estimate the failure rate, software reliability and remaining number of faults in the software during testing phase of software development life cycle. There are numerous SRGMs available in the research literature and in the presence of numerous SRGM, appropriate selection is a staggering task for the software testing professionals. In the software reliability area, researchers have always been interested to select the optimal SRGM to use in a particular situation. In this study, we have developed an integrated technique based on Criteria Importance Through Inter Criteria Correlation (CRITIC) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to select the optimal SRGM. The weights of criteria are obtained by the CRITIC method and SRGMs are ranked by order using TOPSIS method. The working of proposed integrated CRITIC‐TOPSIS approach is illustrated on the two real‐time failure data sets. Furthermore, the methodology validation idea supports the current study by comparing the results obtained with the previous multi‐criteria decision making (MCDM) methodologies namely Additive Ratio Assessment (ARAS). The study revealed that the ranking of SRGMs can be solved effectively using the integration of CRITIC and TOPSIS. The result of this study plays a significant role to take decisions and to evaluate the feasibility of SRGM. [ABSTRACT FROM AUTHOR]

Software reliability literature consists of various change-point-based software reliability growth models and related release time problems. The primary assumption of the existing models is the existence of change-point before software release time only. This does not look practical as the testing team becomes more proficient in detecting the faults due to their continuous involvement in software development by the software release time. Hence the fault detection rate in the pre- and post release phase is not the same. To capture this change in fault detection rate in the pre- and post release testing phase, we propose a new software reliability modeling framework by considering two change-points during the software lifecycle; that is, there exists a change-point before release time and release time as a change-point. Further, in the last one-decade software firms have changed their strategy of stop testing the software after release and continue to test even after release to remove the number of faults to provide better user experiences. This phenomenon attracted academicians to develop theoretical as well empirical study on post release testing and formulation of related release time problem. In this paper, we propose a software cost model to determine optimal release and testing stop time considering under the assumption of two change-points as mentioned above. The proposed model is validated on real-life data set. [ABSTRACT FROM AUTHOR]

Owing to release of software in multiple releases, code changes take place in software. Because of this added complexity in software, the testing team may be unable to correct the fault upon detection, leaving the actual fault to reside in the software, termed as imperfect debugging or there may be replacement of original fault by other fault, leading to error generation. Many other factors exist that affect the testing phase of software like strategies of testing, test cases, skill, efficiency, and learning of testing team. All these factors cannot be kept stable during the whole process of testing. They may change at any time moment causing the background processes to experience change, which is known as change‐point. Keeping all these critical testing environment factors under consideration, a new software reliability growth model has been proposed, which is derived from an non homogenous Poisson process (NHPP)based unified scheme for multi‐release two‐stage fault detection/observation and correction/removal software reliability models. The developed model is numerically illustrated on tandem data set for four releases. [ABSTRACT FROM AUTHOR]