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Found 9 papers, 4 papers with code
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs
This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification.
ADPTriage: Approximate Dynamic Programming for Bug Triage
In this study, we develop a Markov decision process (MDP) model for an online bug triage task.
Time Series Clustering for Grouping Products Based on Price and Sales Patterns
We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns.
Text Classification for Predicting Multi-level Product Categories
In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages.
DABT: A Dependency-aware Bug Triaging Method
In software engineering practice, fixing a bug promptly reduces the associated costs.
A Deep Reinforcement Learning Approach for the Meal Delivery Problem
We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day.
Predicting the Number of Reported Bugs in a Software Repository
We observe that LSTM is effective when considering long-run predictions whereas Random Forest Regressor enriched by exogenous variables performs better for predicting the number of bugs in the short term.
Does chronology matter in JIT defect prediction? A Partial Replication Study
In this work, we aim to investigate the effect of code change properties on JIT models over time.
Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study
Our main goal is to extend prior research and explore the feasibility of HDP and finally to compare its performance with that of its predecessor, Cross-Project Defect Prediction.
