Software testing Resource allocation and release time decisions are vital for the software systems. The objective behind such critical decisions may differ from firm to firm. The motive of the firm may be maximization of software reliability or maximization of number of faults to be removed from each module or it may be minimization of number of faults remaining in the software or minimization of testing resources. Taking into consideration these different aims, various authors have investigated the problem of resource allocation and release time problem. In this paper we investigate various software release policies and resource allocation problem, for example, policies based on the dual constraints of cost and reliability.

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'

Effort distribution plays a major role in software engineering field. Because the limited price projects are becoming common today, the process of effort estimation becomes crucial, to control the budget agreed upon. In last 10 years, numerous software reliability growth models (SRGM) have been developed but majority of model are under static assumption. The basic goal of this article is to explore an optimal resource allocation plan to minimize the software cost throughout the testing phase and operational phase under dynamic condition using genetic algorithm technique. This article also studies the resource allocation problems optimally for various conditions by investigating the activities of the model parameters and also suggests policies for the optimal release time of the software in market place.

In software testing, fault detection and removal process is one of the key elements for quality assurance of the software. In the last three decades, several software reliability growth models were developed for detection and correction of faults. These models were developed under strictly static assumptions. The main goal of this article is to investigate an optimal resource allocation plan for fault detection and removal process of software to minimize cost during testing and operational phase under dynamic condition. For this we develop a mathematical model for fault detection and removal process and Pontryagain's Maximum principle is applied for solving the model. Genetic algorithm is used to find the optimal allocation of fault detection and removal process. Numerical example is also solved for resource allocation for fault detection and remoal process.

Software testing is one of the important steps of SDLC. In software testing one of the important issues is how to allocate the limited resources so that we finish our testing on time and will deliver quality software. Number of Software Reliability Growth Models (SRGM) has been developed for allocating the testing resource in the past three decades but majority of models are developed in static environment. In this paper we developed model in a dynamic environment and also the software is divided into different modules. We also used Pontryagin Maximum principle for solving the model. At last one numerical example is solved for allocating the resource for a given module. For allocating resource optimally we used Genetic Algorithm (GA). GA is used as a powerful tool for solving search & optimization kind of problems.