The vocabulary problem in human-system communication
Communications of the ACM
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
An Information Retrieval Approach to Concept Location in Source Code
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Feature location via information retrieval based filtering of a single scenario execution trace
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
A Traceability Technique for Specifications
ICPC '08 Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension
Source Code Retrieval for Bug Localization Using Latent Dirichlet Allocation
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
A machine learning approach for tracing regulatory codes to product specific requirements
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Bug localization using latent Dirichlet allocation
Information and Software Technology
On the Equivalence of Information Retrieval Methods for Automated Traceability Link Recovery
ICPC '10 Proceedings of the 2010 IEEE 18th International Conference on Program Comprehension
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
Towards mining replacement queries for hard-to-retrieve traces
Proceedings of the IEEE/ACM international conference on Automated software engineering
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Concept location is an essential task during software maintenance and in particular program comprehension activities. One of the approaches to this task is based on leveraging the lexical information found in the source code by means of Information Retrieval techniques. All IR-based approaches to concept location are highly dependent on the queries written by the users. An IR approach, even though good on average, might fail when the input query is poor. Currently there is no way to tell when a query leads to poor results for IR-based concept location, unless a considerable effort is put into analyzing the results after the fact. We propose an approach based on recent advances in the field of IR research, which aims at automatically determining the difficulty a query poses to an IR-based concept location technique. We plan to evaluate several models and relate them to IR performance metrics.