Software Release Planning Using Grey Wolf Optimizer

This chapter presents the application of grey wolf optimizer in software release planning considering warranty based on the proposed mathematical model that measures reliability growth of software systems. Hence, optimal release and warranty time is determined while minimizing the overall software development cost. The software cost model is based on failure phenomenon modelled by incorporating fault removal efficiency, fault reduction factor, and error generation. The model has been validated on the fault dataset of ERP systems. Sensitivity analysis has been carried out to study the discrete changes in the cost parameter due to changes in optimal solution. The work significantly contributes to the literature by fulfilling gaps of reliability growth models, release problems considering warranty, and efficient ways for solving optimization problems. Further, the grey wolf optimizer result has been compared with genetic algorithm and particle swarm optimization techniques.

Software reliability prediction and release time management with 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.

Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering

Software reliability is an essential characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein, we propose a new software reliability modeling method called a deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for the training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

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

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.