A systematic literature review of open source software quality assessment models <s> RQ2. <s> BACKGROUND: A software engineering systematic map is a defined method to build a classification scheme and structure a software engineering field of interest. The analysis of results focuses on frequencies of publications for categories within the scheme. Thereby, the coverage of the research field can be determined. Different facets of the scheme can also be combined to answer more specific research questions. ::: ::: OBJECTIVE: We describe how to conduct a systematic mapping study in software engineering and provide guidelines. We also compare systematic maps and systematic reviews to clarify how to chose between them. This comparison leads to a set of guidelines for systematic maps. ::: ::: METHOD: We have defined a systematic mapping process and applied it to complete a systematic mapping study. Furthermore, we compare systematic maps with systematic reviews by systematically analyzing existing systematic reviews. ::: ::: RESULTS: We describe a process for software engineering systematic mapping studies and compare it to systematic reviews. Based on this, guidelines for conducting systematic maps are defined. ::: ::: CONCLUSIONS: Systematic maps and reviews are different in terms of goals, breadth, validity issues and implications. Thus, they should be used complementarily and require different methods (e.g., for analysis). <s> BIB001 </s> A systematic literature review of open source software quality assessment models <s> RQ2. <s> Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Objective: This research aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. Method: We performed a systematic literature review of empirical studies on ML model published in the last two decades (1991-2010). Results: We have identified 84 primary studies relevant to the objective of this research. After investigating these studies, we found that eight types of ML techniques have been employed in SDEE models. Overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models. Furthermore, different ML models have different strengths and weaknesses and thus favor different estimation contexts. Conclusion: ML models are promising in the field of SDEE. However, the application of ML models in industry is still limited, so that more effort and incentives are needed to facilitate the application of ML models. To this end, based on the findings of this review, we provide recommendations for researchers as well as guidelines for practitioners. <s> BIB002
The methods used by assessment models for selection can be classified as BIB001 BIB002 ): • Data mining technique such as: Artificial Neural Network, Case-Based Reasoning, Data Envelope Analysis (DEA), Fuzzy Logic etc. • Process: A series of actions, or functions leading to a selection result and performing operations on data • Tool based technique: A technique that greatly employs software tools to accomplish selection task • Model: A system representation that allows for selection based on investigation through a hierarchical structure • Framework: A real or conceptual structure intended to serve as support or guide for selection process • Other, e.g. guidelines