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Recovering traceability links in software artifact management systems using information retrieval methods
TLDR
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.
How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms
TLDR
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.
Sentiment Analysis for Software Engineering: How Far Can We Go?
TLDR
This work retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning, and found the results were negative.
Automatic query reformulations for text retrieval in software engineering
TLDR
A recommender (called Refoqus) based on machine learning is proposed, which is trained with a sample of queries and relevant results and automatically recommends a reformulation strategy that should improve its performance, based on the properties of the query.
Release Planning of Mobile Apps Based on User Reviews
TLDR
This paper introduces CLAP (Crowd Listener for releAse Planning), a thorough solution to categorize user reviews based on the information they carry out and automatically prioritize the clusters of reviews to be implemented when planning the subsequent app release.
Cross-project defect prediction models: L'Union fait la force
TLDR
A combined approach is proposed, coined as CODEP (COmbined DEfect Predictor), that employs the classification provided by different machine learning techniques to improve the detection of defect-prone entities.
Mining StackOverflow to turn the IDE into a self-confident programming prompter
TLDR
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.
Mining energy-greedy API usage patterns in Android apps: an empirical study
TLDR
This work presents 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.
An empirical analysis of the distribution of unit test smells and their impact on software maintenance
TLDR
The results show that (i) test smells are widely spread throughout the software systems studied and (ii) most of the test smells have a strong negative impact on the comprehensibility of test suites and production code.
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