most citedPredicting the Number of Reported Bugs in a Software Repository

4 citations · 4 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2021

VARGAN: Variance Enforcing Network Enhanced GAN

Sanaz Mohammadjafari, Mucahit Cevik, Ayse Basar

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite…

cs.IR2021

Text Classification for Predicting Multi-level Product Categories

Hadi Jahanshahi, Ozan Ozyegen, Mucahit Cevik +4

In an online shopping platform, a detailed classification of the products facilitates user navigation. It also helps online retailers keep track of the price fluctuations in a cert…

cs.SE2021

DABT: A Dependency-aware Bug Triaging Method

Hadi Jahanshahi, Kritika Chhabra, Mucahit Cevik +1

In software engineering practice, fixing a bug promptly reduces the associated costs. On the other hand, the manual bug fixing process can be time-consuming, cumbersome, and error-…

cs.SE20214 cited

Predicting the Number of Reported Bugs in a Software Repository

Hadi Jahanshahi, Mucahit Cevik, Ayşe Başar

The bug growth pattern prediction is a complicated, unrelieved task, which needs considerable attention. Advance knowledge of the likely number of bugs discovered in the software s…

cs.SE2021

Does chronology matter in JIT defect prediction? A Partial Replication Study

Hadi Jahanshahi, Dhanya Jothimani, Ayşe Başar +1

Just-In-Time (JIT) models detect the fix-inducing changes (or defect-inducing changes). These models are designed based on the assumption that past code change properties are simil…

cs.SE2021

Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study

Hadi Jahanshahi, Mucahit Cevik, Ayşe Başar

Software defect prediction heavily relies on the metrics collected from software projects. Earlier studies often used machine learning techniques to build, validate, and improve bu…