Research On Constraint-based Software Process Control

Learning from the approach of controlling and improving processes to improve product quality in manufacturing, the idea of controlling the software process to improve software product quality is proposed by software engineering researchers. Software process has become a key factor of software quality. The existing process models are lack of support for the dynamics and uncertainty in software process. In this paper, dynamic software process control methods based on software process theory and constraint programming are studied.In this paper, we propose a TDSPCM model. An organizational standard process framework is defined; an approach for tailoring the standard process methods is proposed; process activities are decomposed into a collection of tasks based on the tailored process framework; tasks and resources are allocated by task-oriented scheduling methods; the process, which is measured through regular review and external changes, is adjusted when the data exceed a given threshold to achieve process control during process implementation; a SPM based on XML is built to realize the standardization process model description, and its application in data exchange is discussed, which provides a solution to data sharing among organizations.The key issue in TDSPCM is scheduling of tasks and resources. Constraint programming is used to model the problem. According to the tasks and tools of software process, the global variables and constraints are determined, a static constraint model is established, and branch and search strategies are discussed. Experimental results demonstrate the effectiveness of the method.We propose two approaches to solve the dynamic constraint model. The characteristics of the dynamic changes are analyzed. Then, ar improved LC is proposed, which uses heuristics and taboo strategy to get better solutions in a short time. The approach aims at minor changes of variables and constraints. Experimental results show that the improved algorithm is better than LC algorithm in solution quality and efficiency. According to the experience-based knowledge of task probability, we propose a proportion buffered approach to generate baseline solution. If changes occur, an adjustment algorithm is used to adjust the base solution A large number of experiments were carried out, and results were analyzed. Experimental results show that the solution obtained by this method has better robustness.