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Feature location in source code: a taxonomy and survey
TLDR
A systematic literature survey of feature location techniques is presented and eighty‐nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature locations.
Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval
TLDR
The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently.
Deep learning code fragments for code clone detection
TLDR
This work introduces learning-based detection techniques where everything for representing terms and fragments in source code is mined from the repository, and compared its approach to a traditional structure-oriented technique and found that it detected clones that were either undetected or suboptimally reported by the prominent tool Deckard.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
TLDR
This paper devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code, which captures a wide range of repair operators without any domain-specific top-down design.
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.
The conceptual cohesion of classes
TLDR
A new set of measures for the cohesion of individual classes within an OO software system is proposed, based on the analysis of the semantic information embedded in the source code, such as comments and identifiers.
Feature location via information retrieval based filtering of a single scenario execution trace
TLDR
A semi-automated technique for feature location in source code based on combining information from two different sources, comparable with previously published approaches and easy to use as it considerably simplifies the dynamic analysis is presented.
Using information retrieval based coupling measures for impact analysis
TLDR
A new set of coupling measures for Object-Oriented (OO) software systems measuring conceptual coupling of classes is presented, which capture new dimensions of coupling, which are not captured by the existing coupling measures.
The Conceptual Coupling Metrics for Object-Oriented Systems
TLDR
A new set of coupling measures for OO systems are presented - named conceptual coupling, based on the semantic information obtained from the source code, encoded in identifiers and comments, which can be used to complement the existing metrics.
Toward Deep Learning Software Repositories
TLDR
This work motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models, and proposes avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts.
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