Fetching the paper…
Reading the bibliography…
Learning-based bug detectors promise to find bugs in large code bases by exploiting natural hints such as names of variables and functions or comments.
1909
Earlier work this paper cites.
M. Daran and P. Thévenod-Fosse, “Software error analysis: A real case study involving real faults and mutations,” in
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
1997
Earlier work this paper cites.
A. Siami Namin, J. H. Andrews, and D. J. Murdoch, “Sufficient mutation operators for measuring test effectiveness,” in
2008
Earlier work this paper cites.
B. H. Smith and L. Williams, “On guiding the augmentation of an automated test suite via mutation analysis,”
2009
Earlier work this paper cites.
R. Just, G. M. Kapfhammer, and F. Schweiggert, “Using non-redundant mutation operators and test suite prioritization to achieve efficient and scalable mutation analysis,” in
2012
Earlier work this paper cites.
A. Hindle, E. T. Barr, Z. Su, M. Gabel, and P. Devanbu, “On the naturalness of software,” in
2012
Earlier work this paper cites.
R. Just, “The major mutation framework: Efficient and scalable mutation analysis for java,” in
2014
Earlier work this paper cites.
R. Just, D. Jalali, L. Inozemtseva, M. D. Ernst, R. Holmes, and G. Fraser, “Are mutants a valid substitute for real faults in software testing?” in
2014
Earlier work this paper cites.
Z. Tu, Z. Su, and P. Devanbu, “On the localness of software,” in
2014
Earlier work this paper cites.
R. Just, D. Jalali, and M. D. Ernst, “Defects4j: A database of existing faults to enable controlled testing studies for java programs,” in
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Earlier work this paper cites.
B. Ray, V. Hellendoorn, S. Godhane, Z. Tu, A. Bacchelli, and P. Devanbu, “On the ”naturalness” of buggy code,” in
2016
Earlier work this paper cites.
M. Allamanis, E. T. Barr, R. Just, and C. Sutton, “Tailored mutants fit bugs better,”
2016
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in
2016
Cited alongside, same era.
V. Raychev, P. Bielik, and M. T. Vechev, “Probabilistic model for code with decision trees,” in
2016
Cited alongside, same era.
R. Just, B. Kurtz, and P. Ammann, “Inferring mutant utility from program context,” in
2017
Cited alongside, same era.
V. J. Hellendoorn and P. Devanbu, “Are deep neural networks the best choice for modeling source code?” in
2017
Cited alongside, same era.
M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” in
2018
Cited alongside, same era.
M. Pradel and K. Sen, “Deepbugs: A learning approach to name-based bug detection,”
2019
Later among the works it cites.
V. J. Hellendoorn, C. Sutton, R. Singh, P. Maniatis, and D. Bieber, “Global relational models of source code,” in
2020
Later among the works it cites.
R.-M. Karampatsis and C. Sutton, “Scelmo: Source code embeddings from language models,”
2020
Later among the works it cites.
K. Clark, M. Luong, Q. V. Le, and C. D. Manning, “ELECTRA: pre-training text encoders as discriminators rather than generators,” in
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…
2018
Cited alongside, same era.
M. Papadakis, D. Shin, S. Yoo, and D.-H. Bae, “Are mutation scores correlated with real fault detection? a large scale empirical study on the relationship between mutants and real faults,” in
2018
Cited alongside, same era.
M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton, “A survey of machine learning for big code and naturalness,”
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. Habib and M. Pradel, “Neural bug finding: A study of opportunities and challenges,”
2019
Cited alongside, same era.
Y. Li, S. Wang, T. N. Nguyen, and S. Van Nguyen, “Improving bug detection via context-based code representation learning and attention-based neural networks,”
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
J. A. Briem, J. Smit, H. Sellik, P. Rapoport, G. Gousios, and M. Aniche, “Offside: Learning to identify mistakes in boundary conditions,” in
2020
Later among the works it cites.
R.-M. Karampatsis, H. Babii, R. Robbes, C. Sutton, and A. Janes, “Big code != big vocabulary: Open-vocabulary models for source code,” in
2020
Later among the works it cites.
R.-M. Karampatsis and C. Sutton, “How often do single-statement bugs occur? the manysstubs4j dataset,” in
2020
Later among the works it cites.
E. Dinella, H. Dai, Z. Li, M. Naik, L. Song, and K. Wang, “Hoppity: Learning graph transformations to detect and fix bugs in programs,” in
2020
Later among the works it cites.
(2021) Spot bugs. [Online]. Available:
2021
Closest in time.
P. Shaw, J. Uszkoreit, and A. Vaswani, “Self-attention with relative position representations,” in
2074
Closest in time.
S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Summarizing source code using a neural attention model,” in
2083
Closest in time.