Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm

Abstract

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).

A novel CRITIC‐TOPSIS approach for optimal selection of software reliability growth model (SRGM)

“…Yang et al 21 Considered random impulsive shocks and statistical analysis method to develop web software reliability model Li et el. 22 Proposed two S-shaped functions describing the growth trend Zhang et al 23 Incorporated rate of mutable fault detection and testing effort function Huang and Kuo 24 Analyzed fault removal process of system software Kumar et al 25 Proposed reliable growth model incorporating software patching Yaghoobi 26 Provided family of Gompertz distribution for fitting software failure times Anand et al 27 Studied the change in reliability due to multi-version software insertion of an infected patch Kumar and Sahni 28 Used optimal control theoretic method to estimate the optimal policy and genetic algorithm for estimating the test effort Liu and Zhao 29 Combined CRITIC and AHP approaches to assess the index weight Bansal et al 30 Designed fuzzy MCDM methods to select software effort estimation model Song and Peng 31 Proposed MCDM method for evaluation of imbalanced classifiers in bankruptcy and credit risk Youssef 32 Incorporated TOPSIS and BWM to rank CSPs Goswami and Mitra 33 Applied ARAS and COPRAS to determine the optimal mobile model from ten alternatives Dahooie et al 34 Used grey additive ratio assessment (ARAS-G) and stepwise weight assessment ratio analysis (SWARA) methods to choose best information technology (IT) Jocic et al 35 Proposed pivot pairwise relative criteria importance assessment (PIPRECIA) method and interval-valued triangular fuzzy additive ratio assessment (ARAS) for the selection of e-learning course Ghenai et al 36 Step-wise Weight Assessment Ratio Analysis/Additive Ratio Assessment (SWARA/ARAS) method for the assessment of sustainability indicators for renewable energy Zavadskas and Turskis 37 ARAS approach to assess microclimate in office rooms Kumar et al 38 Formulated Fuzzy data envelopment analysis (DEA) approach to rank SRGM Sharma et al 39 Developed distance based approach to rank SRGM Kumar et al 40 Developed MCDM based model using entropy and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)…”

“…Multiple distribution functions were considered to represent the different characteristics of the model and weighted criteria approach is discussed to facilitate the selection of the model. Kumar and Sahni 28 suggested a model that provides an overview of the approach used to measure the testing effort in a dynamic environment and assuming that the debugging cost corresponding to each release follows the learning curve phenomenon.…”

A Testing Coverage Based SRGM Subject to the Uncertainty of the Operating Environment

“…Some researchers have studied SRGM by considering a constant fault detection rate [1] or by the learning phenomenon [7]. During the testing and debugging process, various research papers have discussed resource allocation [8][9][10][11][12], testing effort [13,14], etc.…”