48 citations · 61 across the 6 of their papers we have counts for
8 papers
Worst Smells and Their Worst Reasons
Davide Falessi, Rick Kazman
The aims of this paper are: 1) to identify "worst smells", i.e., bad smells that never have a good reason to exist, 2) to determine the frequency, change-proneness, and severity as…
Leveraging the Defects Life Cycle to Label Affected Versions and Defective Classes
Bailey Vandehei, Daniel Alencar da Costa, Davide Falessi
Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers. The aim our study is threefold: 1) to m…
On the Need of Removing Last Releases of Data When Using or Validating Defect Prediction Models
Aalok Ahluwalia, Massimiliano Di Penta, Davide Falessi
To develop and train defect prediction models, researchers rely on datasets in which a defect is attributed to an artifact, e.g., a class of a given release. However, the creation…
Agile Development at Scale: The Next Frontier
Torgeir Dingsøyr, Davide Falessi, Ken Power
Agile methods have transformed the way software is developed, emphasizing active end-user involvement, tolerance to change, and evolutionary delivery of products. The first special…
On the Need of Preserving Order of Data When Validating Within-Project Defect Classifiers
Davide Falessi, Jacky Huang, Likhita Narayana +2
[Context] The use of defect prediction models, such as classifiers, can support testing resource allocations by using data of the previous releases of the same project for predicti…
Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes
Davide Falessi, Justin Roll, Jin Guo +1
As new requirements are introduced and implemented in a software system, developers must identify the set of source code classes which need to be changed. Therefore, past effort ha…
Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation
Sean Bayley, Davide Falessi
Recent studies have shown that tuning prediction models increases prediction accuracy and that Random Forest can be used to construct prediction intervals. However, to our best kno…
Applying empirical software engineering to software architecture: challenges and lessons learned
Davide Falessi, Muhammad Ali Babar, Giovanni Cantone +1
In the last 15 years, software architecture has emerged as an important software engineering field for managing the development and maintenance of large, software- intensive system…