An information retrieval approach to concept location in source code

By Andrian Marcus, Andrey Sergeyev, Vaclav Rajlich, Jonathan I. Maletic. Working Conference on Reverse Engineering, 2004

525 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/dcfaed451a3bd17b2c6dbf0b6b64310d0285e557

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Citations in the corpus

Software Architecture Reconstruction: A Process-Oriented Taxonomy (2009, methodology): “Some approaches use the symbolic information available in the comments [127], [128] or in the method names [89], [102].”
Latent Dirichlet Allocation (2003, methodology): “By using multiple IR approaches such as LDA and LSI, they labelled and summarized source code and compared against human-generated labels.”
Autofolding for Source Code Summarization (2014, methodology): “[46], [47] used Latent Semantic Indexing (LSI) [48] for identifying traceability links and concept location in source code.”
Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval (2007, methodology): “In our experience, developers tend to look at only the first few documents before they formulate new queries [16], [28], [45], [46].”
Retrieval from software libraries for bug localization: a comparative study of generic and composite text models (2011, methodology): “Marcus et al. [20] have investigated IR-based bug localization using the VSM model to represent the source code.”
How developers search for code: a case study (2015, background): “Over 16% of the surveys were concerned with ﬁnding a particular location in the code, also referred to concept location [19].”
Semantic clustering: Identifying topics in source code (2007, methodology): “employed LSI to detect concepts in the code [9].”
On the Use of Automated Text Summarization Techniques for Summarizing Source Code (2010, methodology): “They have also been used to index software corpora in a variety of comprehension applications [13-16].”
Recovering traceability links in software artifact management systems using information retrieval methods (2007, background): “implementing them [Marcus et al. 2004] and combined with structural information to cluster together software components for program comprehension [Maletic and Marcus 2001].”
Exploring the use of deep learning for feature location (2015, unknown): “…used are [8], [9]: • Splitting: separate tokens into constituent words based on common coding style conventions (e.g., the use of camel case or underscores) and on the presence of non-letters (e.g., punctuation or digits) • Normalizing: replace each upper case letter with the corresponding…”
Improving bug localization using structured information retrieval (2013, methodology): “[24] used LSI to find modules related to a given feature in form of a user query.”
Feature location in source code: a taxonomy and survey (2013, methodology): “A preliminary evaluation compared the technique against LSI on a C++ program called iVistaDesktop, which simulates Microsoft’s Windows Vista operating system.”
From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering (2016, background): “Even when the query and the source ﬁles use a mix of statements in natural language and programming language (code snippets in queries and natural language comments in code), the performance of the IR system is sub-optimal due to the signiﬁcant language mismatch.”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, background): “In recent years, many information retrieval based bug localization methods have been proposed [25, 28, 32].”
Bug localization using latent Dirichlet allocation (2010, unknown): “LSI is the application of latent semantic analysis (LSA) to document indexing and retrieval [22] and is the focus of much recent work on source code retrieval [22,27,28].”