Multi attribute architecture design decision for core asset derivation

Software Product Line (SPL) is an effective approach in software reuse in which core assets can be shared among the members of the product line with an explicit treatment of variability. Core assets, which are developed for reuse in domain engineering, are selected for product specific derivation in application engineering. Decision making support during product derivation is crucial to assist in making multiple decisions during product specific derivation. Multiple decisions are to be resolved at the architectural level as well as the detailed design level, address the need for assisting the decision making process during core asset derivation. Architectural level decision making is based on imprecise, uncertain and subjective nature of stakeholder for making architectural selection based on non- functional requirements (NFR). Furthermore, detail design level involves the selection of suitable features which have the rationale behind each decision. The rationale for the selection, if not documented properly, will also result in loss of tacit knowledge. Therefore, a multi-attribute architecture design decision technique is proposed to overcome the above mentioned problem. The technique combines Fuzzy Analytical Hierarchy Process (FAHP) with lightweight architecture design decision documentation to support the decision making during core asset derivation. We demonstrate our approach using the case study of Autonomous Mobile Robot (AMR). The case study implementation shows showed that the proposed technique supports software engineer in the process of decision making at the architecture and detail design levels

Multi-Criteria Decision Making in software development:a systematic literature review

Abstract. Multiple Criteria Decision Making is a formal approach to assist decision makers to select the best solutions among multiple alternatives by assessing criteria which are relatively precise but generally conflicting. The utilization of MCDM are quite popular and common in software development process. In this study, a systematic literature review which includes creating review protocol, selecting primary study, making classification schema, extracting data and other relevant steps was conducted. The objective of this study are making a summary about the state-of-the-art of MCDM in software development process and identifying the MCDM methods and MCDM problems in software development by systematically structuring and analyzing the literature on those issues. A total of 56 primary studies were identified after the review, and 33 types of MCDM methods were extracted from those primary studies. Among them, AHP was defined as the most frequent used MCDM methods in software development process by ranking the number of primary studies which applied it in their studies, and Pareto optimization was ranked in the second place. Meanwhile, 33 types of software development problems were identified. Components selection, design concepts selection and performance evaluation became the three most frequent occurred problems which need to be resolved by MCDM methods. Most of those MCDM problems were found in software design phase. There were many limitations to affect the quality of this study; however, the strictly-followed procedures of SLR and mass data from thousands of literature can still ensure the validity of this study, and this study is also able to provide the references when decision makers want to select the appropriate technique to cope with the MCDM problems

An Analysis and Reasoning Framework for Project Data Software Repositories

As the requirements for software systems increase, their size, complexity and functionality consequently increases as well. This has a direct impact on the complexity of numerous artifacts related to the system such as specification, design, implementation and, testing models. Furthermore, as the software market becomes more and more competitive, the need for software products that are of high quality and require the least monetary, time and human resources for their development and maintenance becomes evident. Therefore, it is important that project managers and software engineers are given the necessary tools to obtain a more holistic and accurate perspective of the status of their projects in order to early identify potential risks, flaws, and quality issues that may arise during each stage of the software project life cycle. In this respect, practitioners and academics alike have recognized the significance of investigating new methods for supporting software management operations with respect to large software projects. The main target of this M.A.Sc. thesis is the design of a framework in terms of, first, a reference architecture for mining and analyzing of software project data repositories according to specific objectives and analytic knowledge, second, the techniques to model such analytic knowledge and, third, a reasoning methodology for verifying or denying hypotheses related to analysis objectives. Such a framework could assist project managers, team leaders and development teams towards more accurate prediction of project traits such as quality analysis, risk assessment, cost estimation and progress evaluation. More specifically, the framework utilizes goal models to specify analysis objectives as well as, possible ways by which these objectives can be achieved. Examples of such analysis objectives for a project could be to yield, high code quality, achieve low production cost or, cope with tight delivery deadlines. Such goal models are consequently transformed into collections of Markov Logic Network rules which are then applied to the repository data in order to verify or deny with a degree of probability, whether the particular project objectives can be met as the project evolves. The proposed framework has been applied, as a proof of concept, on a repository pertaining to three industrial projects with more that one hundred development tasks