• Publications
  • Influence
Recovering traceability links in software artifact management systems using information retrieval methods
TLDR
The main drawback of existing software artifact management systems is the lack of automatic or semi-automatic traceability link generation and maintenance. Expand
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How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms
TLDR
We propose a novel solution called LDA-GA, which uses Genetic Algorithms (GA) to determine a near-optimal configuration for LDA in the context of three different SE tasks: (1) traceability link recovery, (2) feature location, and (3) software artifact labeling. Expand
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Automatic query reformulations for text retrieval in software engineering
TLDR
We propose a recommender (called Refoqus) based on machine learning, which is trained with a sample of queries and relevant results, based on the properties of the query. Expand
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An experimental investigation on the innate relationship between quality and refactoring
TLDR
We mined the evolution history of three Java open source projects to investigate whether refactoring activities occur on code components for which certain indicators—such as quality metrics or the presence of smells as detected by tools—suggest there might be need for refacting operations. Expand
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Cross-project defect prediction models: L'Union fait la force
TLDR
We propose a combined approach, coined as CODEP (COmbined DEfect Predictor), that employs the classification provided by different machine learning techniques to improve the detection of defect-prone entities. Expand
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Release Planning of Mobile Apps Based on User Reviews
TLDR
We introduce CLAP (Crowd Listener for releAse Planning), a thorough solution to (i) categorize user reviews based on the information they carry out (e.g., bug reporting), (ii) cluster together related reviews in a single request, and (iii) recommend which review cluster developers should satisfy in the next release. Expand
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Sentiment Analysis for Software Engineering: How Far Can We Go?
TLDR
We describe our experience in building a software library recommender exploiting crowdsourced opinions mined from Stack Overflow, a state-of-the-art sentiment analysis tool exploiting deep learning. Expand
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Mining energy-greedy API usage patterns in Android apps: an empirical study
TLDR
We present the largest to date quantitative and qualitative empirical investigation into the categories of API calls and usage patterns that—in the context of the Android development framework—exhibit particularly high energy consumption profiles. Expand
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Mining StackOverflow to turn the IDE into a self-confident programming prompter
TLDR
We propose a novel approach 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. Expand
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An empirical study on the developers' perception of software coupling
TLDR
Coupling is a fundamental property of software systems, and numerous coupling measures have been proposed to support various development and maintenance activities. Expand
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