Intelligent Software Engineering Lab

Welcome to ISEL

ISEL aims at producing knowledge on how to improve software development practice based on past experience and evidences elaborated from data recorded in software repositories. 

Recent news:

  • We have two papers on code readability from Carlos Eduardo's PhD. ongoing work: ICSME'2023: How do Developers Improve Code Readability? An Empirical Study of Pull Requests;   SBES'2023: Assessing the Readability of ChatGPT Code Snippet Recommendations: A Comparative Study. Set-2023.
  • A paper from Adriano's thesis has been accepted in the IET Software Journal: "Mining relevant solutions for programming tasks from search engine results". Jun-2023.
  • Marcelo has taken up the position of Coordinator for the Graduate Program in Computer Science. Jun-2023.
  • Adriano Mendonça has defended his PhD thesis and assumed a position as Assistant Professor at Federal University of Uberlandia. Oct-2022
  • Marcelo participated in the examination board of the PhD defense of José Vicente Pereira dos Reis at the University Institute of Lisbon. Sep-2022.
  • Victor Sobreira defended the thesis: "Analysis of Bug Localization Performance Supported by Dataset Dissection" - Jan-2022.
  • The paper "Anti-bloater Class Restructuring: An Exploratory Study" with João Paulo Machado, Elder Sobrinho and Marcelo Maia has been accepted at the Journal of Software: Evolution and Process (Wiley) (Jan-2022).
  • Rodrigo's doctoral thesis (CROKAGE: Effective Solution Recommendation for Programming Tasks by Leveraging Crowd Knowledge)  has been award with as the WINNER of the Software Engineering Doctoral and Master Theses Competition (CTD-ES) (Oct-2021).  


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Best Doctoral Thesis - CBSoft'2021

The doctoral thesis of Rodrigo Fernandes, CROKAGE: Effective Solution Recommendation for Programming Tasks by Leveraging Crowd Knowledge, won the prize of BEST doctoral thesis on Software Engineering at CBSoft'2021 ... more

Bad Smells: Which, When, What, Who, Where

This most comprehensive systematic literature review ever on bad smells includes 351 papers ranging from 1992 to 2017. We show the prevalence of smells in studies, the chronology, the main findings, the shape of collaborations, challenges and much ...more

Defects4J Dissection

Defects4J Dissection presents data to help researchers and practitioners to better understand the Defects4J bug dataset. ...more

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