Results 11 to 20 of about 1,994,533 (253)

Acoustic Emission Waveform Changes for Varying Seeded Defect Sizes. [PDF]

, 2006
The investigation reported in this paper was centered on the application of the Acoustic Emissions (AE) technology for characterising the defect sizes on a radially loaded bearing.
Mba, David, Al-Dossary, Saad, Raja, Hamzah R. I.   +2 more
core   +8 more sources

SODALITE-EU/defect-prediction: M30Release of Defect Predictor

, 2021
This is M30Release of defect prediction support for TOSCA and ...
IndikaKuma, bienstock, indikakumara, Giovanni Quattrocchi, Zoe Vasileiou   +4 more
core   +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
core   +1 more source

SODALITE-EU/defect-prediction: M24Release of Defect Predictor

, 2021
<p>This is M24Release of defect prediction support for TOSCA and Ansible.</p ...
IndikaKuma, Elisabetta Di Nitto, bienstock, indikakumara, Giovanni Quattrocchi, Zoe Vasileiou   +5 more
core   +1 more source

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, Shepperd, M, Shepperd, Martin, Hall, Tracy, Hall, T, Bowes, D   +5 more
core   +1 more source

Automatic Feature Exploration and an Application in Defect Prediction

IEEE Access, 2019
Many software engineering tasks heavily rely on hand-crafted software features, e.g., defect prediction, vulnerability discovery, software requirements, code review, and malware detection.
Yu Qiu, Yun Liu, Ao Liu, Jingwen Zhu, Jing Xu   +4 more
doaj   +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, Mair, Carolyn, Song, Q, Shepperd, M, Shepperd, Martin, Sun, Zhongbin, Song, Qinbao, Sun, Z   +7 more
core   +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

Time to failure prediction in rubber components subjected to thermal ageing: A combined approach based upon the intrinsic defect concept and the fracture mechanics [PDF]

, 2014
In this contribution, we attempt to derive a tool allowing the prediction of the stretch ratioat failure in rubber components subjected to thermal ageing.
NAÏT-ABDELAZIZ, M, MARQUE, Gregory, BEN HASSINE, Mouna, COLIN, Xavier, MARQUE, G, Tourcher, C., Marque, G., Naït-Abdelaziz, M., TOURCHER, C, ZAÏRI, F, Zaïri, F.   +10 more
core   +1 more source

Extending Developer Experience Metrics for Better Effort-Aware Just-In-Time Defect Prediction

IEEE Access, 2022
Developers use defect prediction models to efficiently allocate limited resources for quality assurance and appropriately make a plan for software quality improvement activities.
Yeongjun Cho, Jung-Hyun Kwon, Jooyong Yi, In-Young Ko   +3 more
doaj   +1 more source