Front Cover Caption: The cover image is based on the Research Article Learning to predict test effectiveness by Morteza Zakeri-Nasrabadi and Saeed Parsa https://doi.org/10.1002/int.22722.

The high cost of the test can be dramatically reduced, provided that the coverability as an inherent feature of the code under test is predictable. This article offers a machine learning model to predict the extent to which the test could cover a class in terms of a new metric called Coverageability. The prediction model consists of an ensemble of four regression models. The learning samples consist of feature vectors, where features are source code metrics computed for a class. The samples are labeled by the Coverageability values computed for their corresponding classes. We offer a mathematical model to evaluate test effectiveness in terms of size and coverage of the test suite generated automatically for each class. We extend the size of the feature space by introducing a new approach to define submetrics in terms of existing source code metrics. Using feature importance analysis on the learned prediction models, we sort sources code metrics in the order of their impact on the test effectiveness. As a result of which we found the class strict cyclomatic complexity as the most influential source code metric. Our experiments with our prediction models on a large corpus of Java projects containing about 23,000 classes demonstrate the Mean Absolute Error (MAE) of 0.032, Mean-Squared Error (MSE) of 0.004, and an R2 score of 0.855. Compared with the state-of-the-art coverage prediction models, our models improve MAE, MSE, and an R2 score by 5.78%, 2.84%, and 20.71%, respectively.

In this paper, a novel business process engineering method based on quality assessment is proposed. In the proposed method, a goal model is used to estimate the operational costs of business processes. Goals scenarios in the goal model of desired information systems are applied as a basis for estimating the design cost. Qualities of business requirements models and business process models are also estimated. Based on the quality metrics, the process of business process modeling is examined. Then, using XOR operator in the goal model, a simple and direct mapping of the goal model to the business process model is introduced. Common activities in the business process model are further factored and summarized using pre- and post-factoring operations. The proposed business process modeling method is language-independent. An ICT office in Mazandaran Power Distribution Company is used as a case study to exemplify QABPEM. Our evaluation results demonstrates the capability of the proposed method compared with the existing ones.