Abstract:
“Software testing has a significant importance to achieve maximum quality to satisfy the customers
and concerned stakeholders. A test case is designed to perform set of actions with intend of finding
errors and verify some functions and features of an application. During design process, a huge
number of test cases produced, some of them are of little or no use, which can be ignore or
postponed, when there is budget and time constraints, or a need to decide which test cases to
execute first and which to last. However, in black box testing, test cases are prioritized manually
during requirement analysis and designing phase and companies mostly experience schedule
limitations, in that case, effective testing costs them badly. Test case prioritization’s main purpose
is to effectively use time and budget to execute highest priority test cases first with customer’s
satisfaction.”
Methodology: “To achieve this goal, we proposed a technique in which we use a customer assigned
weight abstracted from business requirements to keep the customer’s preference first, based on
that three main clusters formed. Then we calculate proposed cost and time percentage for each test
case using function points and complexity measure, with in each cluster. Based on that, clusters
further classified in to High, Medium, and Low priorities clusters by K-Medoids algorithm.”
Results: “In our approach, test cases are finally classified into clusters and sub clusters based on
the priority of both stakeholders. Our approach shows 74% accuracy for data set 1 and 73.3%
accuracy for data set 2 as compared to the actual data.”
Conclusion: “To achieve maximum efficiency, considering user’s satisfaction, this method of
mining test cases will be helpful in terms of saving time and cost.”
