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With the growing competition and the demand of the customers, a software organization needs to regularly provide up-gradations and add features to its existing version of software. For the organization, creating these software upgrades means an increase in the complexity of the software which in turn leads to the increase in the number of faults. Also, the faults left undetected in the previous version need to be addressed in this phase. Many software reliability growth models have been proposed to model the phenomenon of multi-release problems using two stage failure observation and correction processes. The model proposed in this paper partitions the fault removal process into a two-stage process which includes fault detection process and fault removal process considering the joint effect of premeditated release pressure and resource restrictions using a well-known Cobb–Douglas production function for the multi release problem of a software. The faults detected in the operational phase of the previous release or left incomplete are also incorporated in the next release. A generalized framework for the multi-release problem in which fault detection follows an exponential distribution function and fault correction follows Gamma distribution function is proposed and verified on a real data set of four releases of software. The estimated parameters and comparison criteria are also given.

Fault detection process (FDP) and Fault correction process (FCP) are important phases of software development life cycle (SDLC). It is essential for software to undergo a testing phase, during which faults are detected and corrected. The main goal of this article is to allocate the testing resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. In this paper, we first assume there is a time lag between fault detection and fault correction. Thus, removal of a fault is performed after a fault is detected. In addition, detection process and correction process are taken to be independent simultaneous activities with different budgetary constraints. A structured optimal policy based on optimal control theory is proposed for software managers to optimize the allocation of the limited resources with the reliability criteria. Furthermore, release policy for the proposed model is also discussed. Numerical example is given in support of the theoretical results.Growing Science Ltd. All rights reserved. 6

PurposeThe use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.Design/methodology/approachThe model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.FindingsThe paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.Originality /valueWe have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

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