Abstract
In this world of software technology, our dependency on software’s is increasing continuously. As a result, software industries are working hard to develop highly reliable software and to meet the expectation of customers. Generally, software companies release software early in market to take gain market share, but rigorous software testing is required for early release software to ensure reliability of software and meet the customer’s expectations. This requires a huge amount of resources, and it increases financial burden on the company, consequently, decreases the overall profit of company. Further, late release due to prolong testing of a software may improves reliability but results into a loss of market opportunity cost or may not be fulfil the customer’s aspirations. As a result, to stay competitive, companies release software early and release patches later to fix the bugs, improve the functionality of software, and to update the software. Software industries are improving the performance or usability of software by releasing patches which may increase the consumption of testing effort and consequently increase in cost. On the other hand, software firms also provide warranty on their products. To address the above said issues, we have developed a testing effort-based software reliability growth model, which incorporates warranty policy and estimates the optimal software release and patch time with the objective to minimise the total testing cost. Further, we have used Genetic Algorithm (GA) to estimate optimum software release and patch time. A numerical illustration has been presented on a real time data set to validate the proposed model.
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