Measuring the Overall Complexity of Graphical and Textual IEC 61131-3 Control Software

TL;DR

This paper proposes a set of objective complexity metrics tailored for IEC 61131-3 control software, validated through industrial case studies, to improve understanding and facilitate refactoring of complex software units.

Contribution

It introduces a novel set of complexity metrics compatible with graphical and textual IEC 61131-3 languages, validated by industrial data, aiding in identifying complex software for refactoring.

Findings

Metrics effectively identify complex software units

Industrial validation confirms metric relevance

Supports better software maintenance and refactoring

Abstract

Software implements a significant proportion of functionality in factory automation. Thus, efficient development and the reuse of software parts, so-called units, enhance competitiveness. Thereby, complex control software units are more difficult to understand, leading to increased development, testing and maintenance costs. However, measuring complexity is challenging due to many different, subjective views on the topic. This paper compares different complexity definitions from literature and considers with a qualitative questionnaire study the complexity perception of domain experts, who confirm the importance of objective measures to compare complexity. The paper proposes a set of metrics that measure various classes of software complexity to identify the most complex software units as a prerequisite for refactoring. The metrics include complexity caused by size, data structure, control flow, information flow and lexical structure. Unlike most literature approaches, the metrics are compliant with graphical and textual languages from the IEC 61131-3 standard. Further, a concept for interpreting the metric results is presented. A comprehensive evaluation with industrial software from two German plant manufacturers validates the metrics' suitability to measure complexity.