• Publications
  • Influence
Is it a bug or an enhancement?: a text-based approach to classify change requests
TLDR
This paper investigates whether the text of the issues posted in bug tracking systems is enough to classify them into corrective maintenance and other kinds of activities and shows that alternating decision trees, naive Bayes classifiers, and logistic regression can be used to accurately distinguish bugs from other kinds.
An Empirical Study of the Impact of Two Antipatterns, Blob and Spaghetti Code, on Program Comprehension
TLDR
It is concluded that developers can cope with one antipattern but that combinations of antip atterns should be avoided possibly through detection and refactorings.
An Exploratory Study of the Impact of Code Smells on Software Change-proneness
TLDR
It is shown that, in almost all releases of Azureus and Eclipse, classes with code smelling are more change-prone than others, and that specific smells are more correlated than others to change-proneness.
Do faster releases improve software quality? An empirical case study of Mozilla Firefox
TLDR
It is found that with shorter release cycles, users do not experience significantly more post-release bugs and bugs are fixed faster, yet users experience these bugs earlier during software execution (the program crashes earlier).
On rapid releases and software testing: a case study and a semi-systematic literature review
TLDR
The changes in software testing effort after moving to rapid releases is investigated in the context of a case study on Mozilla Firefox, and a semi-systematic literature review shows that rapid releases are a prevalent industrial practice that are utilized even in some highly critical domains of software engineering.
Late propagation in software clones
TLDR
This study examines the characteristics of late propagation in two long-lived software systems using the Simian and CCFinder clone detection tools and establishes that some specific cases of late propagations are more harmful than others.
A Bayesian Approach for the Detection of Code and Design Smells
TLDR
This work presents a systematic process to convert existing state-of-the-art detection rules into a probabilistic model and shows that when past detection results are available, this model can be calibrated using machine learning techniques to offer an improved, context-specific detection.
An exploratory study of the impact of antipatterns on class change- and fault-proneness
TLDR
It is shown that, in almost all releases of the four systems, classes participating in antipatterns are more change-and fault-prone than others and size alone cannot explain the higher odds of classes with antip atterns to underwent a (fault-fixing) change than other classes.
Automatic summarization of API reviews
  • Gias Uddin, F. Khomh
  • Computer Science
    32nd IEEE/ACM International Conference on…
  • 30 October 2017
TLDR
It was found that developers were interested to use the proposed summaries much more frequently than other summaries and that while combined with Stack Overflow, Opiner helped developers to make the right decision with more accuracy and confidence and in less time.
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