Software changes are frequent and inevitable. How-ever, erroneous software changes may
cause failures and incidents, degrading user experience and system stability. Thus, it is …

Predicting the classes more likely to change in the future helps developers to focus on the
more critical parts of a software system, with the aim of preventively improving its …

In large-scale online service systems, software changes are inevitable and frequent. Due to
importing new code or configurations, changes are likely to incur incidents and destroy user …

The aim of Just-In-Time (JIT) defect prediction is to predict software changes that are prone
to defects in a project in a timely manner, thereby improving the efficiency of software …

Change impact analysis aims at identifying software artifacts being affected by a change. In
the past, this problem has been addressed by approaches relying on static, dynamic, and …

Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting whether
software changes are defect-inducing or clean based on machine learning classifiers …

Prompt and accurate detection of system anomalies is essential to ensure the reliability of
software systems. Unlike manual efforts that exploit all available run-time information …

Machine Learning is often associated with predictive analytics, for example with the
prediction of buying and termination behavior, with maintenance times or the lifespan of …

In cross-version defect prediction (CVDP), historical data is derived from the prior version of
the same project to predict defects of the current version. Recent studies in CVDP focus on …

In the face of growing vulnerabilities found in open-source software, the need to identify
{discreet} security patches has become paramount. The lack of consistency in how software …