2020
Autores
Koch, I; Ribeiro, C; Lopes, CT;
Publicação
Digital Libraries for Open Knowledge - Lecture Notes in Computer Science
Abstract
2020
Autores
Dias, JP; Lima, B; Faria, JP; Restivo, A; Ferreira, HS;
Publicação
Lecture Notes in Computer Science - Computational Science – ICCS 2020
Abstract
2020
Autores
Lima, B; Faria, JP; Hierons, R;
Publicação
IEEE Access
Abstract
2020
Autores
Raza, M; Faria, JP;
Publicação
IEEE ACCESS
Abstract
High-maturity software development processes and development environments with automated data collection can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, conducting the analysis manually is challenging because of the potentially large amount of data to analyze, the effort and expertise required, and the lack of benchmarks for comparison. In this article, we present ProcessPAIR, a novel method with tool support designed to help developers analyze their performance data with higher quality and less effort. Based on performance models structured manually by process experts and calibrated automatically from the performance data of many process users, it automatically identifies and ranks performance problems and potential root causes of individual subjects, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be appropriately focused. We also show how ProcessPAIR was successfully instantiated and used in software engineering education and training, helping students analyze their performance data with higher satisfaction (by 25%), better quality of analysis outcomes (by 7%), and lower effort (by 4%), as compared to a traditional approach (with reduced tool support).
2020
Autores
Gonçalves, GM; Meneses, R; Faria, JP; Vidal, RM;
Publicação
2020 IEEE Global Engineering Education Conference, EDUCON 2020, Porto, Portugal, April 27-30, 2020
Abstract
2020
Autores
Christley, S; Aguiar, A; Blanck, G; Breden, F; Chan Bukhari, SA; Busse, CE; Jaglale, J; Harikrishnan, SL; Laserson, U; Peters, B; Rocha, A; Schramm, CA; Taylor, S; Vander Heiden, JA; Zimonja, B; Watson, CT; Corrie, B; Cowell, LG;
Publicação
Frontiers Big Data
Abstract
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