Fetching the paper…
Reading the bibliography…
Context: In recent years, leveraging machine learning (ML) techniques has become one of the main solutions to tackle many software engineering (SE) tasks, in research studies (ML4SE).
B. Kitchenham, Procedures for performing systematic reviews, Keele, UK, Keele University 33 (2004) (2004) 1–26
2004
Earlier work this paper cites.
Q. Liu, W. Z. Qin, R. Mintram, M. Ross, Evaluation of preliminary data analysis framework in software cost estimation based on isbsg r9 data, Software quality journal 16 (3) (2008) 411–458
2008
Earlier work this paper cites.
A. E. Khandani, A. J. Kim, A. W. Lo, Consumer credit-risk models via machine-learning algorithms, Journal of Banking & Finance 34 (11) (2010) 2767–2787
2010
Earlier work this paper cites.
H. Lounis, T. F. Gayed, M. Boukadoum, Machine-learning models for software quality: a compromise between performance and intelligibility, in: 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, IEEE, 2011, pp. 919–921
2011
Earlier work this paper cites.
M. P. Basgalupp, R. C. Barros, T. S. da Silva, A. C. de Carvalho, Software effort prediction: A hyper-heuristic decision-tree based approach, in: Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013, pp. 1109–1116
2013
Earlier work this paper cites.
F. Doshi-Velez, Y. Ge, I. Kohane, Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis, Pediatrics 133 (1) (2014) e54–e63
2014
Earlier work this paper cites.
T. Pushphavathi, V. Suma, V. Ramaswamy, A novel method for software defect prediction: hybrid of fcm and random forest, in: 2014 International Conference on Electronics and Communication Systems (ICECS), IEEE, 2014, pp. 1–5
2014
Earlier work this paper cites.
A. Okutan, O. T. Yıldız, Software defect prediction using bayesian networks, Empirical Software Engineering 19 (1) (2014) 154–181
2014
Earlier work this paper cites.
T. Diamantopoulos, A. Symeonidis, Towards interpretable defect-prone component analysis using genetic fuzzy systems, in: 2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, IEEE, 2015, pp. 32–38
2015
Earlier work this paper cites.
T. Mori, Superposed naive bayes for accurate and interpretable prediction, in: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), IEEE, 2015, pp. 1228–1233
2015
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, C. Guestrin, " why should i trust you?" explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016, pp. 1135–1144
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, Advances in neural information processing systems 30 (2017)
2017
Earlier work this paper cites.
H. H. Le, J.-L. Viviani, Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios, Research in International Business and Finance 44 (2018) 16–25
2018
Earlier work this paper cites.
H. K. Dam, T. Tran, T. Pham, S. W. Ng, J. Grundy, A. Ghose, Automatic feature learning for predicting vulnerable software components, IEEE Transactions on Software Engineering 47 (1) (2018) 67–85
2018
Earlier work this paper cites.
T. Ben-Nun, A. S. Jakobovits, T. Hoefler, Neural code comprehension: A learnable representation of code semantics, Advances in Neural Information Processing Systems 31 (2018)
2018
Earlier work this paper cites.
B. Xu, A. Shirani, D. Lo, M. A. Alipour, Prediction of relatedness in stack overflow: deep learning vs. svm: a reproducibility study, in: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2018, pp. 1–10
2018
Earlier work this paper cites.
H. K. Dam, T. Tran, A. Ghose, Explainable software analytics, in: Proceedings of the 40th international conference on software engineering: New ideas and emerging results, 2018, pp. 53–56
2018
Earlier work this paper cites.
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, L. Kagal, Explaining explanations: An overview of interpretability of machine learning, in: 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA), IEEE, 2018, pp. 80–89
2018
Earlier work this paper cites.
Z. C. Lipton, The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery., Queue 16 (3) (2018) 31–57
2018
Earlier work this paper cites.
H. K. Dam, T. Tran, A. Ghose, Explainable software analytics, in: Proceedings of the 40th international conference on software engineering: New ideas and emerging results, 2018, pp. 53–56
2018
Earlier work this paper cites.
C. Tantithamthavorn, A. E. Hassan, K. Matsumoto, The impact of class rebalancing techniques on the performance and interpretation of defect prediction models, IEEE Transactions on Software Engineering 46 (11) (2018) 1200–1219
2018
Earlier work this paper cites.
F. K. Došilović, M. Brčić, N. Hlupić, Explainable artificial intelligence: A survey, in: 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO), IEEE, 2018, pp. 0210–0215
2018
Earlier work this paper cites.
G. Montavon, W. Samek, K.-R. Müller, Methods for interpreting and understanding deep neural networks, Digital signal processing 73 (2018) 1–15
2018
Earlier work this paper cites.
J. Koo, C. Saumya, M. Kulkarni, S. Bagchi, Pyse: Automatic worst-case test generation by reinforcement learning, in: 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST), IEEE, 2019, pp. 136–147
2019
Earlier work this paper cites.
M. White, M. Tufano, M. Martinez, M. Monperrus, D. Poshyvanyk, Sorting and transforming program repair ingredients via deep learning code similarities, in: 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, 2019, pp. 479–490
2019
Cited alongside, same era.
Y. Shido, Y. Kobayashi, A. Yamamoto, A. Miyamoto, T. Matsumura, Automatic source code summarization with extended tree-lstm, in: 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019, pp. 1–8
2019
Cited alongside, same era.
M. Tufano, C. Watson, G. Bavota, M. D. Penta, M. White, D. Poshyvanyk, An empirical study on learning bug-fixing patches in the wild via neural machine translation, ACM Transactions on Software Engineering and Methodology (TOSEM) 28 (4) (2019) 1–29
2019
Cited alongside, same era.
D. Perez, S. Chiba, Cross-language clone detection by learning over abstract syntax trees, in: 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), IEEE, 2019, pp. 518–528
J. He, L. Xu, Y. Fan, Z. Xu, M. Yan, Y. Lei, Deep learning based valid bug reports determination and explanation, in: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), IEEE, 2020, pp. 184–194
2020
Later among the works it cites.
A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al., Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai, Information fusion 58 (2020) 82–115
2020
Later among the works it cites.
F. Ataiefard, M. J. Mashhadi, H. Hemmati, N. Walkinshaw, Deep state inference: Toward behavioral model inference of black-box software systems, IEEE Transactions on Software Engineering (2021)
2021
Later among the works it cites.
X. He, L. Xu, X. Zhang, R. Hao, Y. Feng, B. Xu, Pyart: Python api recommendation in real-time, in: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), IEEE, 2021, pp. 1634–1645
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
E. C. Shin, M. Allamanis, M. Brockschmidt, A. Polozov, Program synthesis and semantic parsing with learned code idioms, Advances in Neural Information Processing Systems 32 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Amershi, A. Begel, C. Bird, R. DeLine, H. Gall, E. Kamar, N. Nagappan, B. Nushi, T. Zimmermann, Software engineering for machine learning: A case study, in: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), IEEE, 2019, pp. 291–300
2019
Cited alongside, same era.
D. Gunning, D. Aha, Darpa’s explainable artificial intelligence (xai) program, AI magazine 40 (2) (2019) 44–58
2019
Cited alongside, same era.
T. Miller, Explanation in artificial intelligence: Insights from the social sciences, Artificial intelligence 267 (2019) 1–38
2019
Cited alongside, same era.
F. Xu, H. Uszkoreit, Y. Du, W. Fan, D. Zhao, J. Zhu, Explainable ai: A brief survey on history, research areas, approaches and challenges, in: CCF international conference on natural language processing and Chinese computing, Springer, 2019, pp. 563–574
2019
Cited alongside, same era.
D. K. Mulligan, J. A. Kroll, N. Kohli, R. Y. Wong, This thing called fairness: Disciplinary confusion realizing a value in technology, Proceedings of the ACM on Human-Computer Interaction 3 (CSCW) (2019) 1–36
2019
Cited alongside, same era.
D. V. Carvalho, E. M. Pereira, J. S. Cardoso, Machine learning interpretability: A survey on methods and metrics, Electronics 8 (8) (2019) 832
2019
Cited alongside, same era.
2021
Later among the works it cites.
A. S. Filippetto, R. Lima, J. L. V. Barbosa, A risk prediction model for software project management based on similarity analysis of context histories, Information and Software Technology 131 (2021) 106497
2021
Later among the works it cites.
W. Gu, Z. Li, C. Gao, C. Wang, H. Zhang, Z. Xu, M. R. Lyu, Cradle: Deep code retrieval based on semantic dependency learning, Neural Networks 141 (2021) 385–394
2021
Later among the works it cites.
N. Miryeganeh, S. Hashtroudi, H. Hemmati, Globug: using global data in fault localization, Journal of Systems and Software 177 (2021) 110961
2021
Later among the works it cites.
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan, A survey on bias and fairness in machine learning, ACM Computing Surveys (CSUR) 54 (6) (2021) 1–35
2021
Later among the works it cites.
S. Suneja, Y. Zheng, Y. Zhuang, J. A. Laredo, A. Morari, Towards reliable ai for source code understanding, in: Proceedings of the ACM Symposium on Cloud Computing, 2021, pp. 403–411
2021
Later among the works it cites.
C. Pornprasit, C. Tantithamthavorn, J. Jiarpakdee, M. Fu, P. Thongtanunam, Pyexplainer: Explaining the predictions of just-in-time defect models, in: 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, 2021, pp. 407–418
2021
Later among the works it cites.
C. Pornprasit, C. K. Tantithamthavorn, Jitline: A simpler, better, faster, finer-grained just-in-time defect prediction, in: 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), IEEE, 2021, pp. 369–379
2021
Later among the works it cites.
J. Jiarpakdee, C. K. Tantithamthavorn, J. Grundy, Practitioners’ perceptions of the goals and visual explanations of defect prediction models, in: 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), IEEE, 2021, pp. 432–443
2021
Later among the works it cites.
W. Zheng, T. Shen, X. Chen, Just-in-time defect prediction technology based on interpretability technology, in: 2021 8th International Conference on Dependable Systems and Their Applications (DSA), IEEE, 2021, pp. 78–89
2021
Later among the works it cites.
M. R. I. Rabin, V. J. Hellendoorn, M. A. Alipour, Understanding neural code intelligence through program simplification, in: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021, pp. 441–452
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Sahakyan, Z. Aung, T. Rahwan, Explainable artificial intelligence for tabular data: A survey, IEEE Access 9 (2021) 135392–135422
2021
Later among the works it cites.
A. Heuillet, F. Couthouis, N. Díaz-Rodríguez, Explainability in deep reinforcement learning, Knowledge-Based Systems 214 (2021) 106685
2021
Later among the works it cites.
2022
Later among the works it cites.
J. E. Zini, M. Awad, On the explainability of natural language processing deep models, ACM Computing Surveys (CSUR) (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Yang, X. Xia, D. Lo, J. Grundy, A survey on deep learning for software engineering, ACM Computing Surveys (CSUR) 54 (10s) (2022) 1–73
2022
Later among the works it cites.
J. Sun, Q. V. Liao, M. Muller, M. Agarwal, S. Houde, K. Talamadupula, J. D. Weisz, Investigating explainability of generative ai for code through scenario-based design, in: 27th International Conference on Intelligent User Interfaces, 2022, pp. 212–228
2022
Later among the works it cites.
B. H. van der Velden, H. J. Kuijf, K. G. Gilhuijs, M. A. Viergever, Explainable artificial intelligence (xai) in deep learning-based medical image analysis, Medical Image Analysis (2022) 102470
2022
Later among the works it cites.
C. Watson, N. Cooper, D. N. Palacio, K. Moran, D. Poshyvanyk, A systematic literature review on the use of deep learning in software engineering research, ACM Transactions on Software Engineering and Methodology (TOSEM) 31 (2) (2022) 1–58
2022
Later among the works it cites.