Code reading is one of the most frequent activities in software maintenance; before implementing changes, it is necessary to fully understand source code often written by other developers. Thus, readability is a crucial aspect of source code that may significantly influence program comprehension effort. In general, models used to estimate software readability take into account only structural aspects of source code, e.g., line length and a number of comments. However, source code is a particular form of text; therefore, a code readability model should not ignore the textual aspects of source code encapsulated in identifiers and comments. In this paper, we propose a set of textual features aimed at measuring code readability. We evaluated the proposed textual features on 600 code snippets manually evaluated (in terms of readability) by 5K+ people. The results demonstrate that the proposed features complement classic structural features when predicting code readability judgments. Consequently, a code readability model based on a richer set of features, including the ones proposed in this paper, achieves a significantly higher accuracy as compared to all of the state-of-the-art readability models.
|Titolo:||Improving code readability models with textual features|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|