Results 291 to 300 of about 198,133 (331)

Deep Semantic Feature Learning for Software Defect Prediction

IEEE Transactions on Software Engineering, 2020
Software defect prediction, which predicts defective code regions, can assist developers in finding bugs and prioritizing their testing efforts. Traditional defect prediction features often fail to capture the semantic differences between different ...
Song Wang, Taiyue Liu, Jaechang Nam, Lin Tan   +3 more
semanticscholar   +1 more source

Software Defect Prediction Based on Deep Representation Learning of Source Code From Contextual Syntax and Semantic Graph

IEEE Transactions on Reliability
Software defect prediction approaches play an essential role in the software development life cycle to help developers predict defects early, thus, preventing wasted time and effort.
Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Redhwan Algabri   +3 more
semanticscholar   +1 more source

Software Defect Prediction Approach Based on a Diversity Ensemble Combined With Neural Network

IEEE Transactions on Reliability
There is a severe class imbalance problem in defect datasets, with nondefective data dominating the distribution, making it easy to generate inaccurate software defect prediction models.
Jinfu Chen, Jiaping Xu, Saihua Cai, Xiaoli Wang, Haibo Chen, Zhehao Li   +5 more
semanticscholar   +1 more source

Progress in Automated Software Defect Prediction

2009
We have designed and implemented a tool that predicts files most likely to have defects in a future release of a large software system. The tool builds a regression model based on the version and defect history of the system, and produces a list of the next release's most probable fault-prone files, sorted in decreasing order of the number of predicted
Thomas J. Ostrand, Elaine J. Weyuker
openaire   +1 more source

Defect prediction on a legacy industrial software

Proceedings of the 4th International Workshop on Conducting Empirical Studies in Industry, 2016
Context: Building defect prediction models for software projects is helpful for reducing the effort in locating defects. In this paper, we share our experiences in building a defect prediction model for a large industrial software project. We extract product and process metrics to build models and show that we can build an accurate defect prediction ...
Yavuz Köroglu, Alper Sen 0001, Doruk Kutluay, Akin Bayraktar, Yalcin Tosun, Murat Çinar, Hasan Kaya   +6 more
openaire   +1 more source

Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction

International Journal of Image and Graphics, 2023
The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality.
M. Prashanthi, M. Mohan
semanticscholar   +1 more source

A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

International Journal of Intelligent Computing and Cybernetics
PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems.
Mohd. Mustaqeem, Suhel Mustajab, Mahfooz Alam   +2 more
semanticscholar   +1 more source

Defect prediction for embedded software

2007 22nd international symposium on computer and information sciences, 2007
As ubiquitous computing becomes the reality of our lives, the demand for high quality embedded software in shortened intervals increases. In order to cope with this pressure, software developers seek new approaches to manage the development cycle: to finish on time, within budget and with no defects.
Atac Deniz Oral, Ayse Basar Bener
openaire   +1 more source

Costs and Benefits of Machine Learning Software Defect Prediction: Industrial Case Study

SIGSOFT FSE Companion
Context: Our research is set in the industrial context of Nokia 5G and the introduction of Machine Learning Software Defect Prediction (ML SDP) to the existing quality assurance process within the company.
Szymon Stradowski, L. Madeyski
semanticscholar   +1 more source

Predicting Software Defects with Explainable Machine Learning

19th Brazilian Symposium on Software Quality, 2020
Most software systems must evolve to cope with stakeholders’ requirements and fix existing defects. Hence, software defect prediction represents an area of interest in both academia and the software industry. As a result, predicting software defects can help the development team to maintain substantial levels of software quality.
Geanderson E. dos Santos, Eduardo Figueiredo 0001, Adriano Veloso, Markos Viggiato, Nivio Ziviani   +4 more
openaire   +1 more source