Using Additive Multiple-Objective Value Functions for Value-Based Software Testing Prioritization

Testing is one way of validating that the customers have specified the right product and verifying that the developers have built the product right. Traditional testing methodologies such as: path, branch, instruction, mutation, scenario, or requirement testing usually treat all aspects of software as equally important [1], and is often reduced to a purely technical issue leaving the close relationship between testing and business decisions unlinked and the potential value contribution of testing unexploited [2]. Valuebased software testing serves to bridge the gap between the internal software testing process and business value that comes from customers and market [3]. The essential of Value-based software testing is feature prioritization that brings business importance that comes from CRACK key customers, software quality risk from developing team, software testing cost, process estimation and controlling from the testing team, and the external commercial factors, e.g., market pressure from the market into consideration. This decision making process could be seen as a multiple-objective decision making process that tries to maximize the business importance, identify the most risky features, minimize the testing cost and minimize the market erosion. The prioritization strategy could be reflected and influenced by different multiple-objective decision value functions. In [3], a multiplicative multiple-objective value function is used to generate the testing priority ranking. In this paper, we will use additive multiple-objective value function to generate testing priority ranking and compare the result in the end.