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Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports
TLDR
The results show that BugLocator can effectively locate the files where the bugs should be fixed, and outperforms existing state-of-the-art bug localization methods. Expand
History Driven Program Repair
TLDR
This work proposes a new technique that utilizes the wealth of bug fixes across projects in their development history to effectively guide and drive a program repair process, and can produce good-quality fixes for many more bugs as compared to the baselines while beingreasonably computationally efficient. Expand
Deep Code Comment Generation
TLDR
DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods. Expand
Towards more accurate retrieval of duplicate bug reports
TLDR
A retrieval function (REP) to measure the similarity between two bug reports, which fully utilizes the information available in a bug report including not only the similarity of textual content in summary and description fields, but also similarity of non-textual fields such as product, component, version, etc. Expand
A discriminative model approach for accurate duplicate bug report retrieval
TLDR
This paper leverages recent advances on using discriminative models for information retrieval to detect duplicate bug reports more accurately and shows that this technique could result in 17--31%, 22--26%, and 35--43% relative improvement over state-of-the-art techniques in OpenOffice, Firefox, and Eclipse datasets respectively using commonly available natural language information only. Expand
Version history, similar report, and structure: putting them together for improved bug localization
TLDR
A new method for locating relevant buggy files that puts together version history, similar reports, and structure is proposed, and a large-scale experiment is performed on four open source projects to localize more than 3,000 bugs. Expand
A learning-to-rank based fault localization approach using likely invariants
TLDR
This work proposes Savant, a new fault localization approach that employs a learning-to-rank strategy, using likely invariant diffs and suspiciousness scores as features, to rank methods based on their likelihood to be a root cause of a failure. Expand
HYDRA: Massively Compositional Model for Cross-Project Defect Prediction
TLDR
Improvements of HYDRA over other baseline approaches in terms of F1-score and when inspecting the top 20 percent lines of code are substantial, and in most cases the improvements are significant and have large effect sizes across the 29 datasets. Expand
Duplicate bug report detection with a combination of information retrieval and topic modeling
TLDR
DBTM is introduced, a duplicate bug report detection approach that takes advantage of both IR-based features and topic- based features, and is able to learn the sets of different terms describing the same technical issues and to detect other not-yet-identified duplicate ones. Expand
Understanding the Test Automation Culture of App Developers
TLDR
Many Android apps are poorly tested and Android app developers use automated testing tools such as JUnit, Monkeyrunner, Robotium, and Robolectric, however, they often prefer to test their apps manually, whereas Windows app developers prefer to use in-house toolssuch as Visual Studio and Microsoft Test Manager. Expand
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