Background: Software product line engineering provides an effective mechanism to implement variable software. However, the usage of preprocessors, which is typical in industry, is heavily criticized, because it often leads to obfuscated code. Using background colors to support comprehensibility has shown effective, however, scalability to large software product lines (SPLs) is questionable. Aim: Our goal is to implement and evaluate scalable usage of background colors for industrial-sized SPLs. Method: We designed and implemented scalable concepts in a tool called FeatureCommander. To evaluate its effectiveness, we conducted a controlled experiment with a large real-world SPL with over 160,000 lines of code and 340 features. We used a within-subjects design with treatments colors and no colors. We compared correctness and response time of tasks for both treatments. Results: For certain kinds of tasks, background colors improve program comprehension. Furthermore, subjects generally favor background colors. Conclusion: We show that background colors can improve program comprehension in large SPLs. Based on these encouraging results, we will continue our work improving program comprehension in large SPLs.

Abstract. Business process management and improvement are vital for enterprises in competitive environments. Understanding of a process is a pre-requisite and important step for improvement. Interaction between humans, computers, and business objects provide excellent opportunities for knowledge extraction. However, the specification of a framework is required for business process improvement, which extends from data collection, analytical methods, storage, and representation of knowledge. The process models conceived for information system development are not sufficient for post execution analysis and improvement. In this paper, we specify such a framework briefly and focus on providing representational support for business process improvement. The main objective is to improve the overall improvement process by providing enriched graphical process models. Furthermore, we use a case study to explain the proposed usage and extensions of an existing modeling language for business process improvement.

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Abstract. Due to the availability of huge number of Web services (WSs), finding an appropriate WS according to the requirement of a service consumer is still a challenge. In this paper, we present a new and flexible approach, called SeqDisc, that assesses the similarity between WSs. In particular, the approach exploits the Prüfer encoding method to represent WSs as sequences capturing both semantic and structure information of service descriptions. Based on the sequence representation, we develop an efficient sequence-based schema matching approach to measure the similarity between WSs. A set of experiments is conducted on real data sets, and the results confirm the performance of the proposed solution.

Process mining is an emerging analysis technique, which extracts process knowledge from data and provides various benefits to organizations. In Service Oriented Computing environment, different services collaborate with others to carry out the operations and therefore overall picture of operations and execution is not clear. Process mining extracts the information from log files of systems, as recorded during executions, and depicts the reality. In order to apply process mining, extraction of process trace data from log files is a prerequisite step. A case study demonstrates the practical applicability of our proposed framework for extraction of the process trace data from application systems and integration portals.