Results 41 to 50 of about 111,109 (312)

Evaluating prediction systems in software project estimation [PDF]

, 2012
This is the Pre-print version of the Article - Copyright @ 2012 ElsevierContext: Software engineering has a problem in that when we empirically evaluate competing prediction systems we obtain conflicting results.
MacDonell, S, Shepperd, M
core   +1 more source

A Survey on Transfer Learning for Cross-Project Defect Prediction

IEEE Access
Software defect prediction involves predicting which components in a software program, like classes or functions, are likely to have defects, based on metrics that describe those components.
Bruno Sotto-Mayor, Meir Kalech
doaj   +1 more source

MSCPDPLab: A MATLAB toolbox for transfer learning based multi-source cross-project defect prediction

SoftwareX, 2023
Software defect prediction (SDP) plays an important role in allocating testing resources and improving testing efficiency. Multi-source cross-project defect prediction (MSCPDP) based on transfer learning refers to transferring defect knowledge from ...
Jiaqi Zou, Zonghao Li, Xuanying Liu, Haonan Tong   +3 more
doaj   +1 more source

Bug or Not? Bug Report Classification Using N-Gram IDF [PDF]

, 2017
Previous studies have found that a significant number of bug reports are misclassified between bugs and non-bugs, and that manually classifying bug reports is a time-consuming task.
Hata, Hideaki, Matsumoto, Kenichi, Phannachitta, Passakorn, Terdchanakul, Pannavat   +3 more
core   +2 more sources

Feature Selection in Cross-Project Software Defect Prediction

Journal of Physics: Conference Series, 2020
Abstract Advances in technology have increased the use and complexity of software. The complexity of the software can increase the possibility of defects. Defective software can cause high losses. Fixing defective software requires a high cost because it can spend up 50% of the project schedule.
A Saifudin, A Trisetyarso, W Suparta, C H Kang, B S Abbas, Y Heryadi   +5 more
openaire   +1 more source

Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework

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, Deepali Gupta, Divya Anand, Nitin Goyal, Hani Moaiteq Aljahdali, Arturo Ortega Mansilla, Seifedine Kadry, Arun Kumar   +7 more
doaj   +1 more source

Learning Effective Changes for Software Projects

, 2017
The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project?
Krishna, Rahul
core   +1 more source

Bellwethers: A Baseline Method For Transfer Learning

, 2018
Software analytics builds quality prediction models for software projects. Experience shows that (a) the more projects studied, the more varied are the conclusions; and (b) project managers lose faith in the results of software analytics if those results
Krishna, Rahul, Menzies, Tim
core   +1 more source

SZZ Unleashed: An Open Implementation of the SZZ Algorithm -- Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project

, 2019
Numerous empirical software engineering studies rely on detailed information about bugs. While issue trackers often contain information about when bugs were fixed, details about when they were introduced to the system are often absent.
Berg, Kristian, Borg, Markus, Hansson, Daniel, Svensson, Oscar   +3 more
core   +1 more source

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

, 2018
We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results ...
Bennin Kwabena Ebo, Chiha I., Ghotra Baljinder, Menzies Tim, Omran M., Pedregosa Fabian, Refaeilzadeh Payam, Tan Ming   +7 more
core   +1 more source