no description available

Naser Al Madi

Colby College

United States

Qing Mi

no description available

Naser Al Madi

Colby College

United States

Qing Mi

Xiaoke Wang

Bingnuo Chen

Automatically assessing code readability is a relatively new challenge that has attracted growing attention from the software engineering community. In this paper, we outline the idea to regard code readability assessment as a learning-to-rank task. Specifically, we design a pairwise ranking model with siamese neural networks, which takes as input a code pair and outputs their readability ranking order. We have evaluated our approach on three publicly available datasets. The result is promising, with an accuracy of 83.5%, a precision of 86.1%, a recall of 81.6%, an F-measure of 83.6% and an AUC of 83.4%.

Qing Mi

Names of classes/methods/variables play an important role in code readability. To investigate how developers choose names, Feitelson et al. conducted an empirical survey and suggested a method to improve naming quality. We replicated their study, but limited the survey subjects to university students. Specifically, we conducted two experiments including 341 students from freshman to senior. The aim of the first experiment was to investigate the characteristics of the names given by students. The experimental results showed that the name length as well as the number of words contained in names increased with the grade level and students have ambiguity in understanding the name. The second experiment was to verify whether Feitelson et al.’s naming method can help improve the quality of names given by students. The experimental data showed an improvement in the quality of names for 70% of cases, which confirms the validity of the method for university students.

Qing Mi

Xiaoke Wang

Bingnuo Chen