The paper motivates, presents, demonstrates in use, and evaluates a methodology for
conducting design science (DS) research in information systems (IS). DS is of importance in …

Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely
used (often interchangeably) by prior studies to derive feature importance ranks from a …

Machine learning techniques have long been used for various purposes in software
engineering. This paper provides a brief overview of the state of the art and reports on a …

Statistical debugging uses dynamic instrumentation and machine learning to identify
predicates on program state that are strongly predictive of program failure. Prior approaches …

Applying prediction models to software engineering is an interesting research area. There
have been many related studies which leverage prediction models to achieve good …

Modern software systems are increasingly configurable. While this has many benefits, it also
makes some software engineering tasks, such as software testing, much harder. This is …

Statistical debugging uses lightweight instrumentation and statistical models to identify
program behaviors that are strongly predictive of failure. However, most software is mostly …

In the context of open source development or software evolution, developers often face test
suites which have been developed with no apparent rationale and which may need to be …

Software systems contain unnecessary code. Its maintenance causes unnecessary costs.
We present tool-support that employs dynamic analysis of deployed software to detect …

In a modern software system, when a program fails, a crash report which contains an
execution trace would be sent to the software vendor for diagnosis. A crash report which …