An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability
| Autor: | Marvin Wyrich, Stefan Wagner, Marvin Muñoz Barón |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Measure (data warehouse) Correctness Source code Computer science business.industry media_common.quotation_subject Cognitive complexity 020207 software engineering Static program analysis 02 engineering and technology computer.software_genre Software metric Software Engineering (cs.SE) Computer Science - Software Engineering 020204 information systems Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Code (cryptography) Artificial intelligence business computer Natural language processing media_common |
| Zdroj: | ESEM |
| Popis: | Background: Developers spend a lot of their time on understanding source code. Static code analysis tools can draw attention to code that is difficult for developers to understand. However, most of the findings are based on non-validated metrics, which can lead to confusion and code, that is hard to understand, not being identified. Aims: In this work, we validate a metric called Cognitive Complexity which was explicitly designed to measure code understandability and which is already widely used due to its integration in well-known static code analysis tools. Method: We conducted a systematic literature search to obtain data sets from studies which measured code understandability. This way we obtained about 24,000 understandability evaluations of 427 code snippets. We calculated the correlations of these measurements with the corresponding metric values and statistically summarized the correlation coefficients through a meta-analysis. Results: Cognitive Complexity positively correlates with comprehension time and subjective ratings of understandability. The metric showed mixed results for the correlation with the correctness of comprehension tasks and with physiological measures. Conclusions: It is the first validated and solely code-based metric which is able to reflect at least some aspects of code understandability. Moreover, due to its methodology, this work shows that code understanding is currently measured in many different ways, which we also do not know how they are related. This makes it difficult to compare the results of individual studies as well as to develop a metric that measures code understanding in all its facets. 12 pages. To be published at ESEM '20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement |
| Databáze: | OpenAIRE |
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