Patch generation is an essential software maintenance task because most software systems
inevitably have bugs that need to be fixed. Unfortunately, human resources are often …

In today's software-centric world, ultra-large-scale software repositories, eg SourceForge
(350,000+ projects), GitHub (250,000+ projects), and Google Code (250,000+ projects) are …

This paper introduces a new technique for finding latent software bugs called change
classification. Change classification uses a machine learning classifier to determine whether …

Many software defect prediction models have been built using historical defect data
obtained by mining software repositories (MSR). Recent studies have discovered that data …

We analyze the version history of 7 software systems to predict the most fault prone entities
and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of …

Machine learning classifiers have recently emerged as a way to predict the introduction of
bugs in changes made to source code files. The classifier is first trained on software history …

Bug-fixes are widely used for predicting bugs or finding risky parts of software. However, a
bug-fix does not contain information about the change that initially introduced a bug. Such …

The ability to evolve software rapidly and reliably is a major challenge for software
engineering. In this introductory chapter we start with a historic overview of the research …

Detection of code clones—similar or identical source code fragments—is of concern both to
researchers and to practitioners. An analysis of the clone detection results for a single …

There have been many bug prediction models built with historical metrics, which are mined
from version histories of software modules. Many studies have reported the effectiveness of …