Can Better Identifier Splitting Techniques Help Feature Location?
Abstract
The paper presents an exploratory study of two feature location techniques utilizing three strategies for splitting identifiers: Camel Case, Samurai and manual splitting of identifiers. The main research question that we ask in this study is if we had a perfect technique for splitting identifiers, would it still help improve accuracy of feature location techniques applied in different scenarios and settings? In order to answer this research question we investigate two feature location techniques, one based on Information Retrieval and the other one based on the combination of Information Retrieval and dynamic analysis, for locating bugs and features using various configurations of preprocessing strategies on two open-source systems, Rhino and jEdit. The results of an extensive empirical evaluation reveal that feature location techniques using Information Retrieval can benefit from better preprocessing algorithms in some cases, and that their improvement in effectiveness while using manual splitting over state-of-the-art approaches is statistically significant in those cases. However, the results for feature location technique using the combination of Information Retrieval and dynamic analysis do not show any improvement while using manual splitting, indicating that any preprocessing technique will suffice if execution data is available. Overall, our findings outline potential benefits of putting additional research efforts into defining more sophisticated source code preproce…
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Random Forest is an effective ensemble learning method for classification, relying on decision tree learning and bagging.
“Random Forest is one of the most effective ensemble learning method for classification, which relies on decision tree learning and bagging [17].”
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Carvalho, N. R., Almeida, J. J., Henriques, P. R., & Pereira, M. J. V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09153-2_9 · Cited by 8 · see citing works
Nostril: A nonsense string evaluator written in Python
Hucka, M. (2018) · The Journal of Open Source Software · doi:10.21105/joss.00596 · Cited by 7 · see citing works
Supporting text retrieval query formulation in software engineering
Haiduc, S. (2013) · Human Biology · Cited by 7 · see citing works
DPWord2Vec: Better Representation of Design Patterns in Semantics
Liu, D., Jiang, H., Li, X., Ren, Z., Qiao, L., & Ding, Z. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3017336 · Cited by 6 · see citing works
Configuring and Assembling Information Retrieval Based Solutions for Software Engineering Tasks
Dit, B. (2016) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme.2016.85 · Cited by 6 · see citing works
Self-citationLog Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding
Fang, Y., Zhao, Z., Xu, Y., & Liu, Z. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033124 · Cited by 5 · see citing works
Jin, D. (2014) · Lincoln (University of Nebraska) · Cited by 3 · see citing works
The effect of automatic concern mapping strategies on conceptual cohesion measurement
da Silva, B. C., Sant’Anna, C., Neylor, R., & Chávez, C. (2016) · Information and Software Technology · doi:10.1016/j.infsof.2016.03.006 · Cited by 3 · see citing works
LogLR: A Log Anomaly Detection Method Based on Logical Reasoning
Zhang, K., Di, X., Liu, X., Li, B., Fang, L., Qin, Y., & Cao, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19214-2_41 · Cited by 3 · see citing works
Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods
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Self-citationSupporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation
Rahman, M. M. (2019) · HARVEST (University of Saskatchewan) · Cited by 2 · see citing works
BLESER: Bug Localization Based on Enhanced Semantic RetrievalPreprint
Zou, W., Li, E., & Fang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03555 · Cited by 2 · see citing works
RELog: Robust and Efficient Anomaly Detection Based on Complete Logs for Large-Scale Systems
Chai, X., Feng, X., Lou, Z., Sun, Y., & Guizani, M. (2026) · IEEE Transactions on Computers · doi:10.1109/tc.2026.3654206 · Cited by 1 · see citing works
Using changeset descriptions as a data source to assist feature location
Chochlov, M., English, M., & Buckley, J. (2015) · IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM) · doi:10.1109/scam.2015.7335401 · Cited by 1 · see citing works
Automating Traceability Link Recovery Using Information Retrieval
MOUNIKA, D. (2016) · i-manager’s Journal on Software Engineering · doi:10.26634/jse.11.1.8191 · Cited by 1 · see citing works
CONTEXT-AWARE SOURCE CODE IDENTIFIER SPLITTING AND EXPANSION FOR SOFTWARE MAINTENANCE
Guerrouj, L. (2013) · PolyPublie (École Polytechnique de Montréal)
Self-citationREPOSITORIES TO CREATE REQUIREMENTS TRACEABILITY LINKS
Ali, N. (2012)
Tracelab: Reproducing Empirical Software Engineering Research
Moritz, E. (2013) · W&M Publish (College of William & Mary) · doi:10.21220/s2-538m-x884
The Impact of Classifier Summary and Classifier Combination on Bug Localization
Navamani, C., & Priyanka, S. (2016) · International Journal of Engineering Trends and Technology · doi:10.14445/22315381/ijett-v33p213
LogNotion: Highlighting Massive Logs to Assist Human Reading and Decision Making
Chu, G., Wang, J., Sun, T., Qi, Q., Sun, H., Zhuang, Z., & Liao, J. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3528327
LogBoost: Boost Log Anomaly Detection by Cherry-Picking Log Sequences
Li, M., Li, X., Chen, P., Lai, Y., & Zheng, Z. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3664358
Ma, J., Xiao, Q., Zhang, Y., He, L., & Chen, H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3685253
- Authors
- Dit, B., Guerrouj, L., Poshyvanyk, D., & Antoniol, G.
- Affiliations
- William & Mary (US)
- Polytechnique Montréal (CA)
- Venue
- 2011 IEEE 19th International Conference on Program Comprehension
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 11-20
- OpenAlex
- W2147296306
- Cited by
- 89
- References
- 38
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 2 · 2019: 8 · 2018: 8 · 2017: 3 · 2016: 7 · 2015: 7 · 2014: 11 · 2013: 16 · 2012: 8
- FWCI
- 8.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Program Comprehension (ICPC) (publishedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- feature location, source code preprocessing, samurai, information retrieval, dynamic analysis, bug localization, rhinoceros, jEdit, open source software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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