International Journal of Software Engineering and Knowledge Engineering

Object-oriented software (OOS) is dominating the software development world today and thus, has to be of high quality and maintainable. However, their recent size and complexity affects the delivering of software products with high quality as well as their maintenance. In the perspective of software maintenance, software change impact analysis (SCIA) is used to avoid performing change in the “dark”. Unfortunately, OOS classes are not without faults and the existing SCIA techniques only predict impact set. The intuition is that, if a class is faulty and change is implemented on it, it will increase the risk of software failure. To balance these, maintenance should incorporate both impact and fault-proneness (FP) predictions. Therefore, this paper propose an extended approach of SCIA that incorporates both activities. The goal is to provide important information that can be used to focus verification and validation efforts on the high risk classes that would probably cause severe failures when changes are made. This will in turn increase maintenance, testing efficiency and preserve software quality. This study constructed a prediction model using software metrics and faults data from NASA data set in the public domain. The results obtained were analyzed and presented. Additionally, a tool called Class Change Recommender (CCRecommender) was developed to assist software engineers compute the risks associated with making change to any OOS class in the impact set.

This work proposes a Web test generation approach based on Stream X-Machines (SXMs). It employs relation matrix to construct test paths (abstract test cases). Two algorithms are presented, one for constructing the length-of-shortest-path matrix and another for establishing the shortest-path matrix from each state to other states in the state transition diagram of a SXM-based specification. For revealing the pre- and post-conditions of test paths conveniently, it transforms the execution of SXM-based abstract test cases into that of Boolean expressions and then tests the Web application under test by using those methods regarding Boolean expressions. Thus, an algorithm is designed to achieve test set for detecting logic connective fault. For a SXM-based abstract test case, the user operations involved are modeled by activity diagrams to derive practical test cases. An experiment on a miniature Web application is carried out to illustrate the SXM-based testing with respect to MC/DC, RC/DC, RMCC and GMCC coverage and in the meanwhile to compare these four criteria on their test effectiveness and fault-detecting ability.

Testing activities for software product lines should be different from that of single software systems, due to significant differences between software product line engineering and single software system development. The cost of testing in software product line is generally higher compared with single software systems; therefore, there should exist a certain balance between cost, quality of final products, and the time of performing testing activities. As decreasing testing cost is an important challenge in software product line integration testing, the contribution of this paper is in introducing a method for early integration testing in software product lines based on feature model (FM) by prioritizing test cases in order to decrease integration testing costs in SPLs. In this method, we focus on reusing domain engineering artifacts and prioritized selection and execution of integration test cases. It also uses separation of concerns and pruning techniques on FMs to help prioritize the test cases. The method shows to be promising when applied to some case studies in the sense that it decreases the costs of performing integration test by about 82% and also detects about 44% of integration faults in domain engineering.

Model-based testing (MBT) seems to be gaining interest in industry and academia due to its provision of systematic, automated and comprehensive testing. The challenge in MBT is to generate optimal test data to execute test cases. Recently, researchers have successfully applied search-based techniques (SBTs) by automating the search for an optimal set of test data at reasonable cost compared to other more expensive techniques. In real complex systems, effectiveness and cost of SBTs for MBT in industrial context are little known. The objective of this study is to empirically evaluate the cost and the effectiveness of SBTs for MBT on industrial case studies. We applied a model-driven approach and SBTs to automatically generate executable feasible test cases. The results show that the model-driven approach generated high number of infeasible test cases with less time while genetic algorithm (GA) and simulating annealing (SA) outperformed significantly random search (RS) with high generation time. We concluded that local SBTs are more appropriate to generate test data when the type of the constraints is simple. Current work on analyzing the cost and effectiveness on SBTs for MBT indicates possible enhancement using the model-driven approach to detect the infeasible paths and SBTs to achieve optimal success rate.

The communication data are becoming increasingly important for criminal network analysis nowadays, and these data provide a digital trace which can be regarded as a hidden clue to support the crack of criminal cases. Additionally, performing a timely and effective analysis on it can predict criminal intents and take efficient actions to restrain and prevent crimes. The primary work of our research is to suggest an analytical process with interactive strategies as a solution to the problem of characterizing criminal groups constructed from the communication data. It is expected to assist law enforcement agencies in the task of discovering the potential suspects and exploring the underlying structures of criminal network hidden behind the communication data. This process allows for network analysis with commonly used metrics to identify the core members. It permits exploration and visualization of the network in the goal of improving the comprehension of interesting microstructures. Most importantly, it also allows to extract community structures in an appropriate level with the label supervision strategy. Our work concludes illustrating the application of our interactive strategies to a real-world criminal investigation with mobile call logs.

In Agile Software Development (ASD) effort estimation plays an important role during release and iteration planning. The state of the art and practice on effort estimation in ASD have been recently identified. However, this knowledge has not yet been organized. The aim of this study is twofold: (1) To organize the knowledge on effort estimation in ASD and (2) to use this organized knowledge to support practice and the future research on effort estimation in ASD. We applied a taxonomy design method to organize the identified knowledge as a taxonomy of effort estimation in ASD. The proposed taxonomy offers a faceted classification scheme to characterize estimation activities of agile projects. Our agile estimation taxonomy consists of four dimensions: estimation context, estimation technique, effort predictors and effort estimate. Each dimension in turn has several facets. We applied the taxonomy to characterize estimation activities of 10 agile projects identified from the literature to assess whether all important estimation-related aspects are reported. The results showed that studies do not report complete information related to estimation. The taxonomy was also used to characterize the estimation activities of four agile teams from three different software companies. The practitioners involved in the investigation found the taxonomy useful in characterizing and documenting the estimation sessions.