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OBJECTIVE-The objective of this paper is to present the current evidence relative to the effectiveness of pair programming (PP) as a pedagogical tool in higher education CS/SE courses. METHOD-We performed a systematic literature review (SLR) of empirical studies that investigated factors affecting the effectiveness of PP for CS/SE students and studies that measured the effectiveness of PP for CS/SE students. RESULTS-Seventy four (74) papers were used in our synthesis of evidence, and 14 compatibility factors that can potentially affect PPʼs effectiveness as a pedagogical tool were identified. Results showed that studentsʼ skill level was the factor that affected PPʼs effectiveness the most. The most common measure used to gauge PPʼs effectiveness was time spent on programming. In addition, studentsʼ satisfaction when using PP was overall higher than when working solo. Our meta-analyses showed that PP was effective in improving studentsʼ grades on assignments. Finally, in the studies that used quality as a measure of effectiveness, the number of test cases succeeded, academic performance, and expert opinion were the quality measures mostly applied. CONCLUSIONS-The results of this SLR show two clear gaps in this research field: i) lack of studies focusing on pair compatibility factors aimed at making PP an effective pedagogical tool; ii) a lack of studies investigating PP for software design/modeling tasks in conjunction with programming tasks.

Context: Ever since the emergence of agile methodologies in 2001, many software companies have shifted to Agile Software Development (ASD), and since then many studies have been conducted to investigate effort estimation within such context; however to date there is no single study that presents a detailed overview of the state of the art in effort estimation for ASD. Objectives: The aim of this study is to provide a detailed overview of the state of the art in the area of effort estimation in ASD. Method: To report the state of the art, we conducted a systematic literature review in accordance with the guidelines proposed in the evidence-based software engineering literature. Results: A total of 25 primary studies were selected; the main findings are: i) Subjective estimation techniques (e.g. expert judgment, planning poker, use case points estimation method) are the most frequently applied in an agile context; ii) Use case points and story points are the most frequently used size metrics respectively; iii) MMRE (Mean Magnitude of Relative Error) and MRE (Magnitude of Relative Error) are the most frequently used accuracy metrics; iv) team skills, prior experience and task size are cited as the three important cost drivers for effort estimation in ASD; and v) Extreme Programming (XP) and SCRUM are the only two agile methods that are identified in the primary studies. Conclusion: Subjective estimation techniques, e.g. expert judgment-based techniques, planning poker or the use case points method, are the one used the most in agile effort estimation studies. As for the size metrics, the ones that were used the most in the primary studies were story points and use case points. Several research gaps were identified, relating to the agile methods, size metrics and cost drivers, thus suggesting numerous possible avenues for future work.

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Background: Many cost estimation papers are based on finding a "new" estimation method, trying out the method on one or two past datasets and "proving" that the new method is better than linear regression. Aim: This paper aims to explain why this approach to model comparison is often invalid and to suggest that the PROMISE repository may be making things worse. Method: We identify some of the theoretical problems with studies that compare different estimation models. We review some of the commonly used datasets from the viewpoint of the reliability of the data and the validity of the proposed linear regression models. Discussion points: It is invalid to select one or two datasets to "prove" the validity of a new technique because we cannot be sure that, of the many published datasets, those chosen are the only ones that favour the new technique. When new models are compared with regression models, researchers need to understand how to use regression analysis appropriately. The use of linear regression presupposes: a linear relationship between dependent and independent variables, no significant outliers, no significant skewness, no relationship between the variance of the dependent variable and the magnitude of the variable. If all these conditions are not true, standard statistical practice is to use a robust regression or transform the data. The logarithmic transformation is appropriate in many cases, and for the Desharnais dataset gives better results than the regression model presented in the PROMISE repository. Conclusions: Simplistic studies comparing data intensive methods with linear regression will be scientifically valueless, if the regression techniques are applied incorrectly. They are also suspect if only a small number of datasets are used and the selection of those datasets is not scientifically justified.

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