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The objective of this paper is to show an interface between modelling, allocation problems and control problems in software reliability and marketing. It has been brought out that in models which exist in software reliability and defined implicitly/explicitly for errors of two types; it is the dependency factor among errors that gives rise to S-Shaped growth curve. It is further shown that this dependency on the line of categorization of errors, adopters can be of several types/categories. Such categorization, though implicit in nature, not brought out in literature, further strengthens the fact that modeling any software phenomenon should incorporate errors of different severity rather explicitly. We have also provided a general model which can account for imperfect debugging and error generation in software reliability. The approach followed is rather implicit in nature but provides good closed form solutions. Such solutions were being alternatively looked for in innovation diffusion models namely the Bass Model, which can account for repeat purchase and growing market size. Besides we also show how problem of resource allocation and control in the two fields can be analyzed similarly. The analogy as brought out in this paper is interesting and may further lead to enriching the two areas of current research.

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Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry.