R
Rocco Oliveto
Researcher at University of Molise
Publications - 220
Citations - 12771
Rocco Oliveto is an academic researcher from University of Molise. The author has contributed to research in topics: Traceability & Source code. The author has an hindex of 58, co-authored 202 publications receiving 10522 citations. Previous affiliations of Rocco Oliveto include University of Salerno.
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Journal ArticleDOI
Recovering traceability links in software artifact management systems using information retrieval methods
TL;DR: An artifact management system with a traceability recovery tool based on Latent Semantic Indexing (LSI), an information retrieval technique, is improved and it is shown that such tools can help to identify quality problems in the textual description of traced artifacts.
Journal ArticleDOI
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)
Michele Tufano,Fabio Palomba,Gabriele Bavota,Rocco Oliveto,Massimiliano Di Penta,Andrea De Lucia,Denys Poshyvanyk +6 more
TL;DR: The findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks, and call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.
Proceedings ArticleDOI
API change and fault proneness: a threat to the success of Android apps
Mario Linares-Vasquez,Gabriele Bavota,Carlos Bernal-Cardenas,Massimiliano Di Penta,Rocco Oliveto,Denys Poshyvanyk +5 more
TL;DR: A study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications' lack of success, estimated from user ratings.
Proceedings ArticleDOI
How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms
Annibale Panichella,Bogdan Dit,Rocco Oliveto,Massimilano Di Penta,Denys Poshynanyk,Andrea De Lucia +5 more
TL;DR: A novel solution to adapt, configure and effectively use a topic modeling technique, namely Latent Dirichlet Allocation (LDA), to achieve better (acceptable) performance across various SE tasks is proposed.
Proceedings ArticleDOI
Mining StackOverflow to turn the IDE into a self-confident programming prompter
TL;DR: A novel approach is proposed that, given a context in the IDE, automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help.