Software Maintenance: Similarity and Inclusion of Rules in Knowledge Extraction

Abstract

Software maintenance is an important phase in the software life cycle. It focuses on keeping the software fully functional and up to date. Maintenance engineers used different approaches and methods to gain understanding of software systems so maintenance tasks can be performed effectively. A lot of efforts have been put into finding a way to measure maintainability of software. It is a common opinion that software maintainability should be described using a set of measurable software attributes. This paper looks at the issue of rule-based description of attributes of software with different levels of maintainability. Varieties of rules are extracted from a data set that represents human evaluation of maintainability of software objects. Rule similarity and rule inclusion measures are used to identify the most diverse sets of rules representing human evaluation criteria. Additionally, the rules representing all evaluators are analyzed using a rule similarity concept in order to learn more about common evaluation criteria