Software Engineering Experimentation

Basic of R Language and RStudio [En]

Estatística Computacional (PtBr) – Universidade Federal do Paraná

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[Ref] A Systematic Literature Review on Fault Prediction Performance in Software Engineering

Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.

This paper appears in: Software Engineering, IEEE Transactions on, Issue Date: Nov.-Dec. 2012, Written by: Hall, T.; Beecham, S.; Bowes, D.; Gray, D.; Counsell, S.

© 2012 IEEE

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[Ref] Preliminary guidelines for empirical research in software engineering

Empirical software engineering research needs research guidelines to improve the research and reporting processes. We propose a preliminary set of research guidelines aimed at stimulating discussion among software researchers. They are based on a review of research guidelines developed for medical researchers and on our own experience in doing and reviewing software engineering research. The guidelines are intended to assist researchers, reviewers, and meta-analysts in designing, conducting, and evaluating empirical studies. Editorial boards of software engineering journals may wish to use our recommendations as a basis for developing guidelines for reviewers and for framing policies for dealing with the design, data collection, and analysis and reporting of empirical studies.

This paper appears in: Software Engineering, IEEE Transactions on , Issue Date: Year Needed , Written by: Kitchenham, Barbara A.; Pfleeger, Shari Lawrence; Pickard, Lesley M.; Jones, Peter W.; Hoaglin, David C.; El Emam, Khaled; Rosenberg, Jarrett

©2002 IEEE

Complete HTML Version: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1027796