Results 31 to 40 of about 198,133 (331)
Applied Sciences, 2022 Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes.
Mahesha Pandit +7 more
doaj +1 more source
Machine Learning Empowered Software Prediction System
Wasit Journal of Computer and Mathematics Science, 2022 Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by
Abdul Syukor Mohamad
doaj +1 more source
Large Defect Prediction Benchmark
, 2022 This is a collection of defect datasets for the software engineering research community. This collection is from 8 corpus as follows: AEEEM-defect-dataset: M. D’Ambros, M. Lanza, and R. Robbes, “Evaluating defect prediction approaches: A benchmark and
Chakkrit (Kla) Tantithamthavorn
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Researcher bias: The use of machine learning in software defect prediction [PDF]
, 2014 This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2014 IEEE. Personal use of this material is permitted.
Bowes, David +5 more
core +1 more source
Predicting software defects with causality tests [PDF]
Journal of Systems and Software, 2014 Abstract In this paper, we propose a defect prediction approach centered on more robust evidences towards causality between source code metrics (as predictors) and the occurrence of defects. More specifically, we rely on the Granger causality test to evaluate whether past variations in source code metrics values can be used to forecast changes in ...
César Couto +4 more
openaire +1 more source
Data quality: Some comments on the NASA software defect datasets [PDF]
, 2013 Background-Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets.
Mair, C +7 more
core +1 more source
Software Defect Prediction Analysis Using Machine Learning Techniques
Sustainability, 2023 There is always a desire for defect-free software in order to maintain software quality for customer satisfaction and to save testing expenses. As a result, we examined various known ML techniques and optimized ML techniques on a freely available data ...
Aimen Khalid +4 more
semanticscholar +1 more source
Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering
IET Software, 2023 Just‐In‐Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability.
Huan Zhang +4 more
doaj +1 more source
A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method
Journal of Intelligence and Information Systems, 2023 Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components.
N. A. A. Khleel, K. Nehéz
semanticscholar +1 more source
An Empirical analysis of Open Source Software Defects data through Software Reliability Growth Models [PDF]
, 2013 The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects.
Najeeb Ullah +3 more
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