Abstract
The wide adoption of component-based software development and the (re)use of software residing in code hosting platforms have led to an increased interest shown towards source code readability and comprehensibility. One factor that can undeniably improve readability is the consistent code styling and formatting used across a project. To that end, many code formatting approaches usually define a set of rules, in order to model a commonly accepted formatting. However, this approach is mostly based on the experts’ expertise, is time-consuming and ignores the specific styling and formatting a team selects to use. Thus, it becomes too intrusive and may be not adopted. In this work, we present an automated mechanism that can be trained to identify deviations from the selected formatting style of a given project, given a set of source code files, and provide recommendations towards maintaining a common styling across all files of the project. At first, source code is transformed into small meaningful pieces, called tokens, which are used to train the models of our mechanism, in order to predict the probability of a token being wrongly positioned. Then, a number of possible fixes are examined as replacements of the wrongly positioned token and, based on a scoring function, the most suitable fixes are given as recommendations to the developer. Preliminary evaluation on various axes indicates that our approach can effectively detect formatting deviations from the project’s code styling and provide actionable recommendations to the developer.
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Karanikiotis, T., Chatzidimitriou, K.C., Symeonidis, A.L. (2022). A Personalized Code Formatter: Detection and Fixing. In: Fill, HG., van Sinderen, M., Maciaszek, L.A. (eds) Software Technologies. ICSOFT 2021. Communications in Computer and Information Science, vol 1622. Springer, Cham. https://doi.org/10.1007/978-3-031-11513-4_8
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