Automated program and software defect root cause analysis using machine learning techniques

Machine Learning Techniques to Predict Software Defect

Machine learning techniques have been dominating in the last two decades. The recently published comprehensive state-of-the-art review (Mohanty et al., 2010) justifies this issue. The ability of software quality models to accurately identify critical faulty components allows for the application of focused verification activities ranging from manual inspection to automated formal analysis method...

On Software Defect Prediction Using Machine Learning

The goal of this paper is to catalog the software defect prediction using machine learning. Over the last few years, the eld of software defect prediction has been extensively studied because of it's crucial position in the area of software reliability maintenance, software cost estimation and software quality assurance. An insurmountable problem associated with software defect prediction is th...

Root Cause and Error Analysis

Error is an inevitable part of life and cannot be completely eliminated, but it can be minimized. A root cause analysis is a technique for understanding the systematic error causes that is involved beyond a person or people to implement an errors and including field and environmental causes of errors when occur in this situation too. An important factor of an error occurrence is a root cause (c...

Techniques for root cause analysis.

Machine learning techniques for automated analysis of facial expressions

Automated analysis of facial expressions paves the way for numerous next-generationcomputing tools including affective computing technologies (proactive and affective user interfaces), learner-adaptive tutoring systems, medical and marketing applications, etc. In this thesis, we propose machine learning algorithms that head toward solving two important but largely understudied problems in autom...