TIDIER: an identifier splitting approach using speech recognition techniques

Abstract

SUMMARYThe software engineering literature reports empirical evidence on the relation between various characteristics of a software system and its quality. Among other factors, recent studies have shown that a proper choice of identifiers influences understandability and maintainability. Indeed, identifiers are developers' main source of information and guide their cognitive processes during program comprehension when high-level documentation is scarce or outdated and when source code is not sufficiently commented. This paper proposes a novel approach to recognize words composing source code identifiers. The approach is based on an adaptation of Dynamic Time Warping used to recognize words in continuous speech. The approach overcomes the limitations of existing identifier-splitting approaches when naming conventions (e.g., Camel Case) are not used or when identifiers contain abbreviations. We apply the approach on a sample of more than 1000 identifiers extracted from 340 C programs and compare its results with a simple Camel Case splitter and with an implementation of an alternative identifier splitting approach, Samurai. Results indicate the capability of the novel approach: (i) to outperform the alternative ones, when using a dictionary augmented with domain knowledge or a contextual dictionary and (ii) to expand 48% of a set of selected abbreviations into dictionary words.

Can Better Identifier Splitting Techniques Help Feature Location?

“…State-of-the-art approaches to split identifiers into separate words are the CamelCase splitter, the Samurai approach proposed by Enslen et al [11], and the recent TIDIER approach [14].…”

“…3) TIDIER: Term IDentifier RecognizER TIDIER [14] is a novel approach to split program identifiers using high-level and domain concepts captured into multiple dictionaries. The approach is based on a thesaurus of words and abbreviations and uses a modified string-edit distance [20] between terms and words as a proxy for the distance between the terms and the concepts they represent.…”

“…Identifier splitting is one of the essential ingredients in any feature location or traceability recovery technique [1,8,21,24,27,29] , since it helps disambiguate conceptual information encoded in compound (or abbreviated) identifiers. The widely adopted approach is based on the CamelCase splitting algorithm, with more sophisticated strategies, such as Samurai [11] and TIDIER [14], recently proposed in the literature.…”

From source code identifiers to natural language terms

“…TIDIER (Madani et al, 2010;Guerrouj et al, 2011) is another approach for identifiers splitting. This algorithm is based in the Dynamic Time Warping algorithm, initially devised to compute distances in the context of speech recognition.…”

“…Typically, many combinations of techniques are used. This is not the case for other languages, like Java for example, where there is a more traditional habit to use CamelCase for example Guerrouj et al (2011). Another relevant detail about these packages is they are quite old, and different programmers have changed the code, increasing the heterogeneity of ways to create identifiers (either by composition or abbreviation).…”