%0 Journal Article
%T Evaluating the Effect of Control Flow on the Unit Testing Effort of Classes: An Empirical Analysis
%A Mourad Badri
%A Fadel Toure
%J Advances in Software Engineering
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/964064
%X The aim of this paper is to evaluate empirically the relationship between a new metric (Quality Assurance Indicator¡ªQi) and testability of classes in object-oriented systems. The Qi metric captures the distribution of the control flow in a system. We addressed testability from the perspective of unit testing effort. We collected data from five open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used different metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required testing effort, in two categories: high and low. In order to evaluate the capability of the Qi metric to predict testability of classes, we used the univariate logistic regression method. The performance of the predicted model was evaluated using Receiver Operating Characteristic (ROC) analysis. The results indicate that the univariate model based on the Qi metric is able to accurately predict the unit testing effort of classes. 1. Introduction Software testing plays a crucial role in software quality assurance. It has, indeed, an important effect on the overall quality of the final product. Software testing is, however, a time and resources consuming process. The overall effort spent on testing depends, in fact, on many different factors including [1¨C5] human factors, process issues, testing techniques, tools used, and characteristics of the software development artifacts. Software testability is an important software quality attribute. IEEE [6] defines testability as the degree to which a system or component facilitates the establishment of test criteria and the performance of tests to determine whether those criteria have been met. ISO [7] defines testability (characteristic of maintainability) as attributes of software that bear on the effort needed to validate the software product. Dealing with software testability raises, in fact, several questions such as [8, 9]: Why is one class easier to test than another? What makes a class hard to test? What contributes to the testability of a class? How can we quantify this notion? Metrics (or models based on metrics) can be used to predict (assess) software testability and better manage the testing effort. Having quantitative data on the testability of a software can, in fact, be used to guide the decision-making of software development managers seeking to produce high-quality software. Particularly, it can help software managers, developers, and testers to [8, 9] plan and monitor testing activities, determine the critical parts of the
%U http://www.hindawi.com/journals/ase/2012/964064/