An Integrated Approach for Measuring Software Quality and Code Readability

In this paper, we explore the concept of code readability and investigate its relation to software quality[1]. This is a new approach to measuring the complexity of software systems[2]. Software industry uses software metrics to measure the complexity of software systems for software cost estimation, software development control, software assurance, software testing, and software maintenance [3], [7], [5]. We explore the concept of code readability and investigate its relation to software quality. With data collected from human annotators, we derive associations between a simple set of local code features and human notions of readability. The paper continues with a framework for evaluating proposed metrics, and applies it to two uses of bug counts. Bug counts capture only a small part of the meaning of the attributes they are being used to measure.[4] We construct an automated readability measure and show that it can be 80% effective, and better than a human on average, at predicting readability judgments. Furthermore, we show that this metric correlates strongly with three measures of software quality: code changes, automated defect reports, and defect log messages. Finally, we discuss the implications of this study on programming.