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Parseweb: a programmer assistant for reusing open source code on the web
Abstract
Programmers commonly reuse existing frameworks or libraries to reduce software development efforts. One common problem in reusing the existing frameworks or libraries is that the programmers know what type of object that they need, but do not know how to get that object with a specific method sequence. To help programmers to address this issue, we have developed an approach that takes queries of the form "Source object type → Destination object type" as input, and suggests relevant method-invocation sequences that can serve as solutions that yield the destination object from the source object given in the query. Our approach interacts with a code search engine (CSE) to gather relevant code samples and performs static analysis over the gathered samples to extract required sequences. As code samples are collected on demand through CSE, our approach is not limited to queries of any specific set of frameworks or libraries. We have implemented our approach with a tool called PARSEWeb, and conducted four different evaluations to show that our approach is effective in addressing programmer's queries. We also show that PARSEWeb performs better than existing related tools: Prospector and Strathcona
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Citations
Cited by
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Raychev, V., Vechev, M., & Yahav, E. (2014) · Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/2594291.2594321 · Cited by 555
Example-centric programming: integrating web search into the development environment
Brandt, J., Dontcheva, M., Weskamp, M., & Klemmer, S. R. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753402 · Cited by 324
Mining StackOverflow to turn the IDE into a self-confident programming prompter
Ponzanelli, L., Bavota, G., Di Penta, M., Oliveto, R., & Lanza, M. (2014) · Proceedings of the 11th Working Conference on Mining Software Repositories · doi:10.1145/2597073.2597077 · Cited by 240
Software Documentation Issues Unveiled
Aghajani, E., Nagy, C., Vega-Marquez, O. L., Linares-Vasquez, M., Moreno, L., Bavota, G., & Lanza, M. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00122 · Cited by 195
Analyzing and mining a code search engine usage log
Bajracharya, S. K., & Lopes, C. V. (2012) · Empirical Software Engineering · doi:10.1007/s10664-010-9144-6 · Cited by 90
A Survey of Machine Learning for Big Code and Naturalness
Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2019) · ACM Computing Surveys · doi:10.1145/3212695 · Cited by 150
Evaluating how developers use general-purpose web-search for code retrieval
Rahman, M. M., Barson, J., Paul, S., Kayani, J., Lois, F. A., Quezada, S. F., Parnin, C., Stolee, K. T., & Ray, B. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196425 · Cited by 62
How Practitioners Expect Code Completion?
Wang, C., Hu, J., Gao, C., Jin, Y., Xie, T., Huang, H., Lei, Z., & Deng, Y. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616280 · Cited by 50
Self-citationSupporting Software Developers with a Holistic Recommender System
Ponzanelli, L., Scalabrino, S., Bavota, G., Mocci, A., Oliveto, R., Di Penta, M., & Lanza, M. (2017) · 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2017.17 · Cited by 41
Terra, R., Valente, M. T., Czarnecki, K., & Bigonha, R. S. (2015) · Software: Practice and Experience · doi:10.1002/spe.2228 · Cited by 37
Active Inductive Logic Programming for Code Search
Sivaraman, A., Zhang, T., Van den Broeck, G., & Kim, M. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00044 · Cited by 34
Towards Understanding Successful Novice Example Use in Blocks-Based Programming
Ichinco, M., Harms, K., & Kelleher, C. (2017) · Journal of Visual Languages and Sentient Systems · doi:10.18293/vlss2017-012 · Cited by 10
Metadata
- Authors
- Thummalapenta, S., & Xie, T.
- Venue
- Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
- Publisher
- ACM
- Published
- 2007-11-05
- Pages
- 204-213
- OpenAlex
- W2106259924
- Cited by
- 391
- References
- 0
- FWCI
- 14.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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