Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

By Denys Poshyvanyk, Yann-Gaël Guéhéneuc, Andrian Marcus, G. Antoniol, Vaclav Rajlich. IEEE Transactions on Software Engineering, 2007

487 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/5aa9a530dee277bd40dbe8b081b8025439174ad1

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Citations in the corpus

Exploring the use of deep learning for feature location (2015, methodology): “[14], for an effectiveness measure we use the rank of the first relevant document, which represents the number of source code entities a developer would have to view before reaching a relevant one.”
A Systematic Survey of Program Comprehension through Dynamic Analysis (2008, background): “[51]; in the same context, Wilde et al.”
From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering (2016, methodology): “[35, 36] use LSI to reduce the dimension of the term-document matrix,”
Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “First, 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…”
Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “Poshyvanyk et al. [37, 38] introduce PROMESIR, which combines LSI and execution scenario based probabilistic ranking method, to locate bugs for Mozilla and Eclipse systems.”
How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms (2013, methodology): “The performance of LDA in this scenario was analyzed using the effectiveness measure (EM) [36].”
Bug localization using latent Dirichlet allocation (2010, methodology): “Case Study 1 examined whether the accuracy of LDA-based bug localization is better than LSI, over the same data used in previous LSI studies [28].”
Feature location in source code: a taxonomy and survey (2013, methodology): “Cerberus uses PROMESIR to combine rankings of program elements from execution traces with rankings from IR to produce seeds for PDA.”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, methodology): “Poshyvanyk et al. [31] presented a feature location method called PROMESIR, which combines results from both dynamic analysis and information retrieval.”
Feature-Oriented Software Product Lines (2013, background): “For yet other concerns of the system that are currently not configurable, no tracing information may be present in the source code; in such cases, tracing links can be extracted manually or with semi-automatic feature-location tools (Biggerstaff et al. 1993; Robillard and Murphy 2002; Poshyvanyk et ”
Improving bug localization using structured information retrieval (2013, background): “[27] formulated the feature location problem as a decision-making problem in the presence of uncertainty.”