Nonsense Variable Names Detection Method Based on Lexical Features and Data Mining
Identifiers is an important part of code,and it is also one of the key elements for people to understand the semantics of code.Variables are widely used to represent objects in programs.Names of such variables could serve as a major clue to the re-sponsibility of the variables if they are serious and properly named.However,unqualified variable names(e.g.,"a","var")are constructed frequently by developers.Such nonsense variable names have a severe negative impact on the readability and maintai-nability of software applications.So,automated identification of bad smells is one of the hot topics in the field of software refacto-ring.To identify such nonsense names automatically,we conduct an empirical study to figure out the key features that could be exploited to distinguishing nonsense names from well-constructed meaningful ones.Results of the study suggest that nonsense variable names are often short and rarely contain meaningful words.To this end,in this paper,we propose a heuristics and data mining-based approach to identifying nonsense variable names.It first retrieves suspicious variable names based on lexical analy-sis.On the resulting suspicious names,it conducts an abbreviation expansion-based filtering to exclude such variable names that are carefully constructed to represent the abbreviations of meaningful words.Finally,it conducts data mining-based filtering to further exclude well-known symbols(e.g."i","e").Experimental results on open source datasets show that the proposed method has high accuracy.Its average precision and recall is 85％and 91.5％,respectively.
Software refactoringCode qualityData miningNonsense variable namesLexical features
NETL
NSTL
万方数据
