Identification of high-level concept clones in source code

By Andrian Marcus, Jonathan I. Maletic. International Conference on Automated Software Engineering, 2001

321 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8a3c20b35d57b86802621b3399e2a5960780aac0

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Citations in the corpus

Recovering documentation-to-source-code traceability links using latent semantic indexing (2003, methodology): “[21] Marcus, A. and Maletic, J. I., "Identification of High-Level Concept Clones in Source Code", in Proceedings Automated Software Engineering (ASE'01), San Diego, CA, November 26- 29 2001, pp. 107-114.”
An information retrieval approach to concept location in source code (2004, methodology): “In our earlier work, we used LSI to identify abstract data types and high-level concept clones in procedural code [21] [22].”
Semantic code search via equational reasoning (2020, methodology): “Rattan et al identify 8 tools [8, 15, 26, 32, 34, 36, 44, 74] as focusing on semantic clone detection (often called “type-4 clones" in the literature [66]), including 3 of the PDG-based tools.”
Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval (2007, methodology): “In software engineering, LSI has been used for many tasks closely related to feature location, such as reuse [32], [33], [34], abstract data types identification [35], clone detection [36], traceability link recovery between the source code and an external documentation [30], [37], conceptual coupli”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, unknown): “Marcus and duplicate code Maletic[8] used latent semantic analysis to identify related source files (what they called concept clones).”
Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “…we have extensive experience in using LSI for other software engineering problems like concept and feature location [60], [66], [68], traceability link recovery between source code and documentation [58], identification of abstract data types in legacy source code [54], and clone detection [57].”
Survey of Research on Software Clones (2006, methodology): “[34] compare certain pieces of text, namely, identifiers using latent semantic indexing, a technique from information retrieval.”
Comparison and Evaluation of Clone Detection Tools (2007, unknown): “Ç”
Comparison and evaluation of code clone detection techniques and tools: A qualitative approach (2009, methodology): “Marcus and Maletic [86] apply latent semantic indexing (LSI) to source text in order to ﬁnd high level concept clones, such as abstract data types (ADTs), in the source code.”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, unknown): “The typical categories are text-based [16, 38], token-based [9, 27, 34, 42], tree-based [11, 25, 18], and graph-based [31, 36].”
Clone Detection Using Abstract Syntax Suffix Trees (2006, result): “The paper reports the results of several large case studies in which we empirically compare the new technique to other techniques using the Bellon benchmark for clone detectors.”
Semantic clustering: Identifying topics in source code (2007, methodology): “In the field of software engineering LSI has been successfully applied to categorized source files [18] and opensource projects [19], detect high-level conceptual clones [20], recover links between external documentation and source code [21,22] and to compute the class cohesion [22].”