A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Abstract

Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters. General Terms Evolutionary Algorithm KeywordsGenetic Algorithm, testing effort allocation, Software reliability, SRGM Nomenclature a is the initial fault content in the software. b is the fault detection rate.is the number of fault removed at time 't'. m(t) is the cumulative number of fault detected till time 't' due to the testing effort w1(t).T the planning period.C1(m(t),w2(t)) Cost per unit at time 't' for cumulative faults removed m(t) and debugging effort w2(t).C 2 is the cost of testing per unit testing efforts.W is the total resources utilized during the SDLC at any point of time 't'.W 1 (t) current effort expenditure due to testing at time 't' W 2 (t) current effort expenditure due to debugging the fault at time 't'

Software Testing Resource Allocation and Release Time Problem: A Review

“…Prashant Johri, Md. Nasar and Udayan Chanda [36] Genetic Algorithm Distribute total testing resources optimally under dynamic condition using optimal control theory and genetic algorithm. Md Nasar, prashant johri and Udayan chanda [37] Differential Evolution Distribute total testing resources optimally under dynamic condition.…”