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Identifier names convey useful information about the intended semantics ofcode. Name-based program analyses use this information, e.g., to detect bugs,to predict types, and to improve the readability of code. At the core ofname-based analyses are semantic representations of identifiers, e.g., in theform of learned embeddings. The high-level goal of such a representation is toencode whether two identifiers, e.g., len and size, are semantically similar.Unfortunately, it is currently unclear to what extent semantic representationsmatch the semantic relatedness and similarity perceived by developers. Thispaper presents IdBench, the first benchmark for evaluating semanticrepresentations against a ground truth created from thousands of ratings by 500software developers. We use IdBench to study state-of-the-art embeddingtechniques proposed for natural language, an embedding technique specificallydesigned for source code, and lexical string distance functions. Our resultsshow that the effectiveness of semantic representations varies significantlyand that the best available embeddings successfully represent semanticrelatedness. On the downside, no existing technique provides a satisfactoryrepresentation of semantic similarities, among other reasons becauseidentifiers with opposing meanings are incorrectly considered to be similar,which may lead to fatal mistakes, e.g., in a refactoring tool. Studying thestrengths and weaknesses of the different techniques shows that they complementeach other. As a first step toward exploiting this complementarity, we presentan ensemble model that combines existing techniques and that clearlyoutperforms the best available semantic representation.

IdBench: Evaluating Semantic Representations of Identifier Names in Source Code