Fetching the paper…
Reading the bibliography…
Our research investigates the recommendation of code examples to aid software developers, a practice that saves developers significant time by providing ready-to-use code snippets.
Siegel, S. (1956). Nonparametric statistics for the behavioral sciences
1956
Earlier work this paper cites.
Basili, V. R., Selby, R. W. & Hutchens, D. H. (1986). Experimentation in software engineering. IEEE Transactions on software engineering, (7), 733–743
1986
Earlier work this paper cites.
Marascuilo, L. A. & Serlin, R. C. (1988). Statistical methods for the social and behavioral sciences. WH Freeman/Times Books/Henry Holt & Co
1988
Earlier work this paper cites.
Wilcoxon, F. (1992). Individual comparisons by ranking methods (pp. 196-202). Springer New York
1992
Earlier work this paper cites.
Medsker, L. R., & Jain, L. C. (2001). Recurrent neural networks. Design and Applications, 5, 64-67
2001
Earlier work this paper cites.
Moonen, L. (2001). Generating robust parsers using island grammars. Proceedings eighth working conference on reverse engineering, pp. 13–22
2001
Earlier work this paper cites.
Rogers, I. (2002). The Google Pagerank algorithm and how it works
2002
Earlier work this paper cites.
Charikar, M. S. (2002). Similarity estimation techniques from rounding algorithms. Proceedings of the thirty-fourth annual ACM symposium on Theory of computing, pp. 380–388
2002
Earlier work this paper cites.
2003
Earlier work this paper cites.
Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. S. (2004, June). Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the twentieth annual symposium on Computational geometry (pp. 253-262)
2004
Earlier work this paper cites.
Wang, J., & Han, J. (2004, April). BIDE: Efficient mining of frequent closed sequences. In Proceedings. 20th international conference on data engineering (pp. 79-90). IEEE
2004
Earlier work this paper cites.
Bettenburg, N., Premraj, R., Zimmermann, T. & Kim, S. (2008). Extracting structural information from bug reports. Proceedings of the 2008 international working conference on Mining software repositories, pp. 27–30
2008
Earlier work this paper cites.
Raychev, V., Vechev, M. & Yahav, E. (2014). Code completion with statistical language models. Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 419–428. via mining open source code on the web. 2008 23rd IEEE/ACM International Conference on Automated Software Engineering, pp. 327–336
2008
Earlier work this paper cites.
Bacchelli, A., Cleve, A., Lanza, M. & Mocci, A. (2011). Extracting structured data from natural language documents with island parsing. 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), pp. 476–479
2011
Cited alongside, same era.
Rigby, P. C. & Robillard, M. P. (2013). Discovering essential code elements in informal documentation. 2013 35th International Conference on Software Engineering (ICSE), pp. 832–841
2013
Cited alongside, same era.
Wang, J., Dang, Y., Zhang, H., Chen, K., Xie, T. & Zhang, D. (2013). Mining succinct and high-coverage API usage patterns from source code. 2013 10th Working Conference on Mining Software Repositories (MSR), pp. 319–328
2013
Cited alongside, same era.
Diamantopoulos, T. & Symeonidis, A. (2015). Employing source code information to improve question-answering in stack overflow. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, pp. 454–457
2015
Cited alongside, same era.
Zhou, S., Shen, B., & Zhong, H. (2019, November). Lancer: Your code tell me what you need. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1202-1205). IEEE
2019
Later among the works it cites.
Nguyen, P. T., Di Rocco, J., Di Ruscio, D., Ochoa, L., Degueule, T. & Di Penta, M. (2019). Focus: A recommender system for mining api function calls and usage patterns. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pp. 1050–1060
2019
Later among the works it cites.
Aytekin, A. M. & Aytekin, T. (2019). Real-time recommendation with locality sensitive hashing. Journal of Intelligent Information Systems, 53(1), 1–26
2019
Later among the works it cites.
Rubei, R., Di Sipio, C., Nguyen, P. T., Di Rocco, J. & Di Ruscio, D. (2020). PostFinder: Mining Stack Overflow posts to support software developers. Information and Software Technology, 127, 106367
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Huang, Q., Feng, J., Zhang, Y., Fang, Q. & Ng, W. (2015). Query-aware locality-sensitive hashing for approximate nearest neighbor search. Proceedings of the VLDB Endowment, 9(1), 1–12
2015
Cited alongside, same era.
Gu, X., Zhang, H., Zhang, D. & Kim, S. (2016). Deep API learning. Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering, pp. 631–642
2016
Cited alongside, same era.
Ding, S. H., Fung, B. C. & Charland, P. (2016). Kam1n0: Mapreduce-based assembly clone search for reverse engineering. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 461–470
2016
Cited alongside, same era.
Abdalkareem, R., Shihab, E. & Rilling, J. (2017). On code reuse from stackoverflow: An exploratory study on android apps. Information and Software Technology, 88, 148–158. a code-to-code search engine. Proceedings of the 40th International Conference on Software Engineering, pp. 946–957
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Kim, K., Kim, D., Bissyandé, T. F., Choi, E., Li, L., Klein, J. & Traon, Y. L. (2018). FaCoY: a code-to-code search engine. Proceedings of the 40th International Conference on Software Engineering, pp. 946–957
2018
Cited alongside, same era.
Zhang, K., Fan, S. & Wang, H. J. (2018). An efficient recommender system using locality sensitive hashing. Proceedings of the 51st Hawaii International Conference on System Sciences
2018
Cited alongside, same era.
Diamantopoulos, T., Karagiannopoulos, G. & Symeonidis, A. L. (2018). Codecatch: extracting source code snippets from online sources. Proceedings of the 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, pp. 21–27
2018
Cited alongside, same era.
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., … & Rush, A. M. (2020, October). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations (pp. 38-45)
2020
Later among the works it cites.
Di Rocco, J., Di Ruscio, D., Di Sipio, C., Nguyen, P. T., & Rubei, R. (2021). Development of recommendation systems for software engineering: the CROSSMINER experience. Empirical Software Engineering, 26(4), 69
2021
Later among the works it cites.
Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452, 48-62
2021
Later among the works it cites.
A. Naghshzan, L. Guerrouj and O. Baysal, "Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow," 2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM), Luxembourg, 2021, pp. 142-152, doi: 10.1109/SCAM52516.2021.00026
2021
Later among the works it cites.
2022
Later among the works it cites.
Silavong, F., Moran, S., Georgiadis, A., Saphal, R. & Otter, R. (2022). Senatus-A Fast and Accurate Code-to-Code Recommendation Engine. 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), pp. 511–523
2022
Later among the works it cites.
2023
Closest in time.
Rahmani, S. (2023). Towards recommending code examples using informal documentation (Doctoral dissertation, École de technologie supérieure)
2023
Closest in time.