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Variable names are important to understand and maintain code. If a variablename and the value stored in the variable do not match, then the programsuffers from a name-value inconsistency, which is due to one of two situationsthat developers may want to fix: Either a correct value is referred to througha misleading name, which negatively affects code understandability andmaintainability, or the correct name is bound to a wrong value, which may causeunexpected runtime behavior. Finding name-value inconsistencies is hard becauseit requires an understanding of the meaning of names and knowledge about thevalues assigned to a variable at runtime. This paper presents Nalin, atechnique to automatically detect name-value inconsistencies. The approachcombines a dynamic analysis that tracks assignments of values to names with aneural machine learning model that predicts whether a name and a value fittogether. To the best of our knowledge, this is the first work to formulate theproblem of finding coding issues as a classification problem over names andruntime values. We apply Nalin to 106,652 real-world Python programs, wheremeaningful names are particularly important due to the absence of staticallydeclared types. Our results show that the classifier detects name-valueinconsistencies with high accuracy, that the warnings reported by Nalin have aprecision of 80% and a recall of 76% w.r.t. a ground truth created in a userstudy, and that our approach complements existing techniques for finding codingissues.

Nalin: Learning from Runtime Behavior to Find Name-Value Inconsistencies in Jupyter Notebooks