An Assessment of Testing Cost With Effort-Dependent FDP and FCP Under Learning Effect: A Genetic Algorithm Approach

Abstract

Software testing involves verification and validation of the software to meet the requirements elucidated by customers in the earlier phases and to subsequently increase software reliability. Around half of the resources, such as manpower and CPU time are consumed and a major portion of the total cost of developing the software is incurred in testing phase, making it the most crucial and time-consuming phase of a software development lifecycle (SDLC). Also the fault detection process (FDP) and fault correction process (FCP) are the important processes in SDLC. A number of software reliability growth models (SRGM) have been proposed in the last four decades to capture the time lag between detected and corrected faults. But most of the models are discussed under static environment. The purpose of this paper is to allocate the resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. An elaborate optimization policy based on optimal control theory for resource allocation with the objective to minimize the cost is proposed. Further, genetic algorithm is applied to obtain the optimum value of detection and correction efforts which minimizes the cost. Numerical example is given in support of the above theoretical result. The experimental results help the project manager to identify the contribution of model parameters and their weight.

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

“…The pattern in Fig. 2 exhibits the same pattern as in Kumar et al (2014) due the nature of Eq. (1) and Eq.…”

“…(1) and Eq. (2) but the values are different from the Kumar et al (2014). The same is signified by the gap between the two lines, i.e., between the lines showing faults detected and faults corrected.…”

“…During software testing life cycle (STLC), we have two separate teams; a team of tester for detection purpose and another team of debugger for correction purpose. Kumar et al (2014) assumed that detection and correction effort are interdependent and they have divided total available resources into two portions for detection and correction.…”

“…We have incorporated Pegels (1969) functional form for detection and correction cost function, which demonstrates the learning curve phenomenon. Kumar et al (2014) used Pegels (1969 and further applied Genetic Algorithm to find the optimum value of single control variable. Many researcher, scientists and engineers used this functional form to demonstrate the concept of learning curve phenomenon in their pioneer work in the field of management, economics, engineering and science.…”

“…Kapur et al (2010Kapur et al ( , 2013 did not mention the concept of time lag during the modeling. Kumar et al (2014) proposed a resource allocation model for detection and correction purpose with fixed budgetary constraint with the assumption that detection and correction are two concurrent activities. Also, they have assumed detection effort and correction effort are interdependent.…”