This paper aims to provide a basis for the improvement of software-estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A Web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) increase the breadth of the search for relevant studies, 2) search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) conduct more studies on estimation methods commonly used by the software industry, and 4) increase the awareness of how properties of the data sets impact the results when evaluating estimation methods

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A Systematic Review of Software Development Cost Estimation Studies

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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.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Cost%20Effort%20Estimation/Wen%20-%20software%20development%20effort%20estimation%20models%20-%202012.pdf

Systematic literature review of machine learning based software development effort estimation models

Business data mining-a machine learning perspective

27 p. ; Includes bibliographical references (pp. 25-27). ; "January 2004."; The authors would like to thank the research contribution made by our research associate Amer Mohamed who was responsible for assisting us in the coding of all of the articles and citations for this study.

/pdf/meta-review-of-knowledge-management-and-intellectual-capital-6infezorkp.pdf

Meta-review of knowledge management and intellectual capital literature: citation impact and research productivity rankings

Software development cost estimation integrating neural network with cluster analysis

Many existing solution methodologies for machine assignment problems in group technology do not consider factors such as part demand, operation sequence and cost of intercellular moves We formulate a 0-1 quadratic programming model that takes into account these factors in machine assignment Two approaches are proposed to solve this problem The first is an A*-based approach that generates optimal solutions The second is a heuristic approach developed to solve problems with large number of machines and/or parts The heuristic approach is shown to be efficient in producing good solutions in a computational study

Solving the Generalized Machine Assignment Problem in Group Technology

Designing group technology manufacturing systems using heuristics branching rules

A significant and growing area of business-computing research is concerned with AI. Knowledge about which journals are the most influential forums for disseminating AI research is important for business school faculty, students, administrators, and librarians. To date, there has been only one study attempting to rank AI journals from a business-computing perspective. It used a subjective methodology, surveying opinions of business faculty about a prespecified list of 30 journals. Here, we report the results of a more objective study. We conducted a citation analysis covering a time period of 5 years to compile 15,600 citations to 1,244 different journals. Based on these data, the journals are ranked in two ways involving the magnitude and the duration of scientific impact each has had in the field of AI.

Citation-Based Journal Rankings for AI Research A Business Perspective

■A significant and growing area of business-com-puting research is concerned with AI. Knowledgeabout which journals are the most influential fo-rums for disseminating AI research is importantfor business school faculty, students, administra-tors, and librarians. To date, there has been onlyone study attempting to rank AI journals from abusiness-computing perspective. It used a subjec-tive methodology, surveying opinions of businessfaculty about a prespecified list of 30 journals.Here, we report the results of a more objectivestudy. We conducted a citation analysis coveringa time period of 5 years to compile 15,600 cita-tions to 1,244 different journals. Based on thesedata, the journals are ranked in two ways involv-ing the magnitude and the duration of scientificimpact each has had in the field of AI.

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Anita Lee

Papers

Software development cost estimation integrating neural network with cluster analysis

Solving the Generalized Machine Assignment Problem in Group Technology

Designing group technology manufacturing systems using heuristics branching rules

Citation-Based Journal Rankings for AI Research A Business Perspective

Citation-Based Journal Rankings for AI Research