The best of both worlds: integrating semantic features with expert features for defect prediction and localization
To improve software quality, just-in-time defect prediction (JIT-DP)(identifying defect-
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …
The impact of feature selection techniques on effort‐aware defect prediction: An empirical study
Abstract Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …
the defect density and guide the testing team to inspect the modules with high defect density …
Fine-grained just-in-time defect prediction at the block level in infrastructure-as-code (iac)
Infrastructure-as-Code (IaC) is an emerging software engineering practice that leverages
source code to facilitate automated configuration of software systems' infrastructure. IaC files …
source code to facilitate automated configuration of software systems' infrastructure. IaC files …
A multi-objective effort-aware defect prediction approach based on NSGA-II
Abstract Effort-Aware Defect Prediction (EADP) technique sorts software modules by the
defect density and aims to find more bugs when testing a certain number of Lines of Code …
defect density and aims to find more bugs when testing a certain number of Lines of Code …
On the relative value of clustering techniques for unsupervised effort-aware defect prediction
P Yang, L Zhu, Y Zhang, C Ma, L Liu, X Yu… - Expert Systems with …, 2024 - Elsevier
Abstract Unsupervised Effort-Aware Defect Prediction (EADP) uses unlabeled data to
construct a model and ranks software modules according to the software feature values. Xu …
construct a model and ranks software modules according to the software feature values. Xu …
Federated Learning for Software Engineering: A Case Study of Code Clone Detection and Defect Prediction
In various research domains, artificial intelligence (AI) has gained significant prominence,
leading to the development of numerous learning-based models in research laboratories …
leading to the development of numerous learning-based models in research laboratories …
Boosting multi-objective just-in-time software defect prediction by fusing expert metrics and semantic metrics
Just-in-time software defect prediction (JIT-SDP) aims to predict whether a code commit is
defect-inducing or defect-clean immediately after developers submit their code commits. In …
defect-inducing or defect-clean immediately after developers submit their code commits. In …
Distinguishing LLM-generated from Human-written Code by Contrastive Learning
X Xu, C Ni, X Guo, S Liu, X Wang, K Liu… - ACM Transactions on …, 2024 - dl.acm.org
Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted
significant attention from both industry and academia due to their demonstrated ability to …
significant attention from both industry and academia due to their demonstrated ability to …
Unifying Defect Prediction, Categorization, and Repair by Multi-Task Deep Learning
Just-In-Time defect prediction models can identify defect-inducing commits at check-in time
and many approaches are proposed with remarkable performance. However, these …
and many approaches are proposed with remarkable performance. However, these …
An exploratory study on just-in-time multi-programming-language bug prediction
Context: An increasing number of software systems are written in multiple programming
languages (PLs), which are called multi-programming-language (MPL) systems. MPL bugs …
languages (PLs), which are called multi-programming-language (MPL) systems. MPL bugs …