TECHNOLOGY OF IDENTIFYING ANTIPATTERNS IN ANDROID PROJECTS WRITTEN IN KOTLIN LANGUAGE
Abstract
The problem of the lack of instruments for identifying the characteristics of low-quality code in Android projects that are written in the Kotlin language is determined. A review of modern approaches for identifying antipatterns in program code is accomplished. The analysis of the methods used to find problems with code in Android projects is performed. DECOR and Paprika approaches are considered. Conclusions are drawn about the importance of finding design flaws in program code for the mobile software development and its further support. An antipatterns identification approach for Kotlin language program code in Android projects is proposed. An algorithm for identifying low-quality Kotlin code is presented. The technology for detecting poor quality code characteristics consists of four stages: collecting metrics about an analyzed software system, building a quality model, converting a quality model into a graph representation, and identifying predefined antipatterns. The collection of metrics, including the search for both Androidspecific and object-oriented metrics of Chidamber and Kamerer, is proposed to be implemented through parsing source code and converting it into an abstract syntax tree using the KASTree library. The implementation of KASTree library usage is offered through the Adapter design pattern. The construction of a quality model is implemented using the Paprika tool, supplemented by a number of introduced metrics. Conversion of quality model exactly into graph representation is used to identify antipatterns in order to ensure the speed and quality of complex queries execution for identifying antipatterns. Antipatterns identification using database queries is based on various template rules, including the Catolino rules. Different features of applying the Cypher query language to a graph database are used to represent the rules in form of queries. Results of the work can be used in development of software for poor quality code identification in mobile applications written in Kotlin language, as well as in studies of mobile development antipatterns for this language.
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