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2020 – today
- 2024
[j33]Michael Fu, Chakkrit Tantithamthavorn
, Trung Le, Yuki Kume, Van Nguyen, Dinh Q. Phung, John C. Grundy:
AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities. Empir. Softw. Eng. 29(1): 4 (2024)- [j32]Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan:
Ethics in AI through the practitioner's view: a grounded theory literature review. Empir. Softw. Eng. 29(3): 67 (2024) - [j31]Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Yuan-Fang Li:
Syntax-aware on-the-fly code completion. Inf. Softw. Technol. 165: 107336 (2024) - [j30]Chanathip Pornprasit, Chakkrit Tantithamthavorn:
Fine-tuning and prompt engineering for large language models-based code review automation. Inf. Softw. Technol. 175: 107523 (2024) - [j29]Yang Hong, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Aldeida Aleti:
Don't forget to change these functions! recommending co-changed functions in modern code review. Inf. Softw. Technol. 176: 107547 (2024) - [j28]Saranya Alagarsamy, Chakkrit Tantithamthavorn, Aldeida Aleti:
A3Test: Assertion-Augmented Automated Test case generation. Inf. Softw. Technol. 176: 107565 (2024) - [j27]Michael Fu, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Phung, Trung Le:
Vision Transformer Inspired Automated Vulnerability Repair. ACM Trans. Softw. Eng. Methodol. 33(3): 78:1-78:29 (2024) - [j26]Aastha Pant, Rashina Hoda, Simone V. Spiegler, Chakkrit Tantithamthavorn, Burak Turhan:
Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges. ACM Trans. Softw. Eng. Methodol. 33(3): 80:1-80:35 (2024) - [j25]Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo:
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues. ACM Trans. Softw. Eng. Methodol. 33(5): 116:1-116:26 (2024) - [j24]Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu, Li Li:
On the Reliability and Explainability of Language Models for Program Generation. ACM Trans. Softw. Eng. Methodol. 33(5): 126:1-126:26 (2024) - [j23]Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Dinh Q. Phung:
Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection. ACM Trans. Softw. Eng. Methodol. 33(6): 162 (2024) - [c36]Wannita Takerngsaksiri, Cleshan Warusavitarne, Christian Yaacoub, Matthew Hee Keng Hou, Chakkrit Tantithamthavorn:
Students' Perspectives on AI Code Completion: Benefits and Challenges. COMPSAC 2024: 1606-1611 - [c35]Danushka Liyanage, Seongmin Lee, Chakkrit Tantithamthavorn, Marcel Böhme:
Extrapolating Coverage Rate in Greybox Fuzzing. ICSE 2024: 132:1-132:12 - [c34]Yang Hong, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Patanamon Thongtanunam, Arik Friedman, Xing Zhao, Anton Krasikov:
Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian. SIGSOFT FSE Companion 2024: 370-381 - [i41]Wannita Takerngsaksiri, Rujikorn Charakorn, Chakkrit Tantithamthavorn, Yuan-Fang Li:
TDD Without Tears: Towards Test Case Generation from Requirements through Deep Reinforcement Learning. CoRR abs/2401.07576 (2024) - [i40]Chanathip Pornprasit, Chakkrit Tantithamthavorn:
GPT-3.5 for Code Review Automation: How Do Few-Shot Learning, Prompt Design, and Model Fine-Tuning Impact Their Performance? CoRR abs/2402.00905 (2024) - [i39]Yang Hong, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Patanamon Thongtanunam, Arik Friedman, Xing Zhao, Anton Krasikov:
Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian. CoRR abs/2402.09651 (2024) - [i38]Saranya Alagarsamy, Chakkrit Tantithamthavorn, Chetan Arora, Aldeida Aleti:
Enhancing Large Language Models for Text-to-Testcase Generation. CoRR abs/2402.11910 (2024) - [i37]Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan:
Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development. CoRR abs/2403.15481 (2024) - [i36]Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn:
AI for DevSecOps: A Landscape and Future Opportunities. CoRR abs/2404.04839 (2024) - [i35]Aastha Pant, Rashina Hoda, Burak Turhan, Chakkrit Tantithamthavorn:
What do AI/ML practitioners think about AI/ML bias? CoRR abs/2407.08895 (2024) - [i34]Patanamon Thongtanunam, Chakkrit Tantithamthavorn:
Code Ownership: The Principles, Differences, and Their Associations with Software Quality. CoRR abs/2408.12807 (2024) - 2023
- [j22]Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu:
Deep Learning for Android Malware Defenses: A Systematic Literature Review. ACM Comput. Surv. 55(8): 153:1-153:36 (2023) - [j21]Chakkrit Tantithamthavorn, Jürgen Cito, Hadi Hemmati, Satish Chandra:
Explainable AI for SE: Challenges and Future Directions. IEEE Softw. 40(3): 29-33 (2023) - [j20]Jürgen Cito, Satish Chandra, Chakkrit Tantithamthavorn, Hadi Hemmati:
Expert Perspectives on Explainability. IEEE Softw. 40(3): 84-88 (2023) - [j19]Rashina Hoda, Hoa Khanh Dam, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Margaret-Anne D. Storey:
Augmented Agile: Human-Centered AI-Assisted Software Management. IEEE Softw. 40(4): 106-109 (2023) - [j18]Chanathip Pornprasit, Chakkrit Kla Tantithamthavorn:
DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction. IEEE Trans. Software Eng. 49(1): 84-98 (2023) - [j17]Michael Fu, Chakkrit Tantithamthavorn:
GPT2SP: A Transformer-Based Agile Story Point Estimation Approach. IEEE Trans. Software Eng. 49(2): 611-625 (2023) - [j16]Michael Fu, Van Nguyen, Chakkrit Kla Tantithamthavorn, Trung Le, Dinh Q. Phung:
VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types. IEEE Trans. Software Eng. 49(10): 4550-4565 (2023) - [c33]Chakkrit Tantithamthavorn, Norman Chen:
Unit Testing Challenges with Automated Marking. APSEC 2023: 544-548 - [c32]Michael Fu, Chakkrit Kla Tantithamthavorn, Van Nguyen, Trung Le:
ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? APSEC 2023: 632-636 - [c31]Wei Teo, Ze Teoh, Dayang Abang Arabi, Morad Aboushadi, Khairenn Lai, Zhe Ng, Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan:
What Would You do? An Ethical AI Quiz. ICSE Companion 2023: 112-116 - [c30]Danushka Liyanage, Marcel Böhme, Chakkrit Tantithamthavorn, Stephan Lipp:
Reachable Coverage: Estimating Saturation in Fuzzing. ICSE 2023: 371-383 - [c29]Haonan Hu, Yue Liu, Yanjie Zhao, Yonghui Liu, Xiaoyu Sun, Chakkrit Tantithamthavorn, Li Li:
Detecting Temporal Inconsistency in Biased Datasets for Android Malware Detection. ASEW 2023: 17-23 - [c28]Ahmad Haji Mohammadkhani, Chakkrit Tantithamthavorn, Hadi Hemmati:
Explaining Transformer-based Code Models: What Do They Learn? When They Do Not Work? SCAM 2023: 96-106 - [c27]Chanathip Pornprasit, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Chunyang Chen:
D-ACT: Towards Diff-Aware Code Transformation for Code Review Under a Time-Wise Evaluation. SANER 2023: 296-307 - [i33]Ahmad Haji Mohammadkhani, Nitin Sai Bommi, Mariem Daboussi, Onkar Sabnis, Chakkrit Tantithamthavorn, Hadi Hemmati:
A Systematic Literature Review of Explainable AI for Software Engineering. CoRR abs/2302.06065 (2023) - [i32]Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu, Li Li:
On the Reliability and Explainability of Automated Code Generation Approaches. CoRR abs/2302.09587 (2023) - [i31]Saranya Alagarsamy, Chakkrit Tantithamthavorn, Aldeida Aleti:
A3Test: Assertion-Augmented Automated Test Case Generation. CoRR abs/2302.10352 (2023) - [i30]Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Yuki Kume, Van Nguyen, Dinh Phung, John C. Grundy:
AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities. CoRR abs/2305.16615 (2023) - [i29]Michael Fu, Trung Le, Van Nguyen, Chakkrit Tantithamthavorn, Dinh Q. Phung:
Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities. CoRR abs/2306.06109 (2023) - [i28]Aastha Pant, Rashina Hoda, Simone V. Spiegler, Chakkrit Tantithamthavorn, Burak Turhan:
Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges. CoRR abs/2307.10057 (2023) - [i27]Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach Dinh Le, David Lo:
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues. CoRR abs/2307.12596 (2023) - [i26]Chakkrit Tantithamthavorn, Norman Chen:
Unit Testing Challenges with Automated Marking. CoRR abs/2310.06308 (2023) - [i25]Michael Fu, Chakkrit Tantithamthavorn, Van Nguyen, Trung Le:
ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We? CoRR abs/2310.09810 (2023) - [i24]Xinyu She, Yue Liu, Yanjie Zhao, Yiling He, Li Li, Chakkrit Tantithamthavorn, Zhan Qin, Haoyu Wang:
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey. CoRR abs/2310.17903 (2023) - [i23]Wannita Takerngsaksiri, Cleshan Warusavitarne, Christian Yaacoub, Matthew Hee Keng Hou, Chakkrit Tantithamthavorn:
Students' Perspective on AI Code Completion: Benefits and Challenges. CoRR abs/2311.00177 (2023) - 2022
- [j15]Anjana Perera, Aldeida Aleti, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Burak Turhan, Lisa Kuhn, Katie Walker:
Search-based fairness testing for regression-based machine learning systems. Empir. Softw. Eng. 27(3): 79 (2022) - [j14]Jirayus Jiarpakdee, Chakkrit Kla Tantithamthavorn, Hoa Khanh Dam, John C. Grundy:
An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models. IEEE Trans. Software Eng. 48(2): 166-185 (2022) - [j13]Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, Kenichi Matsumoto:
Predicting Defective Lines Using a Model-Agnostic Technique. IEEE Trans. Software Eng. 48(5): 1480-1496 (2022) - [j12]Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John Grundy, Wray L. Buntine:
SQAPlanner: Generating Data-Informed Software Quality Improvement Plans. IEEE Trans. Software Eng. 48(8): 2814-2835 (2022) - [j11]Dayi Lin, Chakkrit Tantithamthavorn, Ahmed E. Hassan:
The Impact of Data Merging on the Interpretation of Cross-Project Just-In-Time Defect Models. IEEE Trans. Software Eng. 48(8): 2969-2986 (2022) - [c26]Patanamon Thongtanunam, Chanathip Pornprasit, Chakkrit Tantithamthavorn:
AutoTransform: Automated Code Transformation to Support Modern Code Review Process. ICSE 2022: 237-248 - [c25]Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu:
Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well? ISSRE 2022: 169-180 - [c24]Michael Fu, Chakkrit Tantithamthavorn:
LineVul: A Transformer-based Line-Level Vulnerability Prediction. MSR 2022: 608-620 - [c23]Yang Hong, Chakkrit Tantithamthavorn, Patanamon Thongtanunam, Aldeida Aleti:
CommentFinder: a simpler, faster, more accurate code review comments recommendation. ESEC/SIGSOFT FSE 2022: 507-519 - [c22]Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, Dinh Q. Phung:
VulRepair: a T5-based automated software vulnerability repair. ESEC/SIGSOFT FSE 2022: 935-947 - [c21]Yang Hong, Chakkrit Kla Tantithamthavorn, Patanamon Thongtanunam:
Where Should I Look at? Recommending Lines that Reviewers Should Pay Attention To. SANER 2022: 1034-1045 - [i22]Sherlock A. Licorish, Christoph Treude, John C. Grundy, Chakkrit Tantithamthavorn, Kelly Blincoe, Stephen G. MacDonell, Li Li, Jean-Guy Schneider:
Software Engineering in Australasia. CoRR abs/2206.05397 (2022) - [i21]Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan:
Ethics in AI through the Developer's Prism: A Socio-Technical Grounded Theory Literature Review and Guidelines. CoRR abs/2206.09514 (2022) - [i20]Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu:
Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well? CoRR abs/2209.00812 (2022) - [i19]Yue Liu, Chakkrit Tantithamthavorn, Yonghui Liu, Patanamon Thongtanunam, Li Li:
AutoUpdate: Automatically Recommend Code Updates for Android Apps. CoRR abs/2209.07048 (2022) - [i18]Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Hung Nguyen, Dinh Q. Phung:
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle. CoRR abs/2209.10406 (2022) - [i17]Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John C. Grundy, Hung Nguyen, Seyit Camtepe, Paul Quirk, Dinh Q. Phung:
An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability. CoRR abs/2209.10414 (2022) - [i16]Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Yuan-Fang Li:
Syntax-Aware On-the-Fly Code Completion. CoRR abs/2211.04673 (2022) - [i15]Ahmad Haji Mohammadkhani, Chakkrit Tantithamthavorn, Hadi Hemmati:
Explainable AI for Pre-Trained Code Models: What Do They Learn? When They Do Not Work? CoRR abs/2211.12821 (2022) - 2021
- [j10]Sherlock A. Licorish, Christoph Treude, John C. Grundy, Kelly Blincoe, Stephen G. MacDonell, Chakkrit Tantithamthavorn, Li Li, Jean-Guy Schneider:
Software Engineering in Australasia. ACM SIGSOFT Softw. Eng. Notes 46(2): 16-17 (2021) - [j9]Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John Grundy:
Actionable Analytics: Stop Telling Me What It Is; Please Tell Me What To Do. IEEE Softw. 38(4): 115-120 (2021) - [j8]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Ahmed E. Hassan:
The Impact of Correlated Metrics on the Interpretation of Defect Models. IEEE Trans. Software Eng. 47(2): 320-331 (2021) - [c20]Chun Yong Chong, Patanamon Thongtanunam, Chakkrit Tantithamthavorn:
Assessing the Students' Understanding and their Mistakes in Code Review Checklists: An Experience Report of 1, 791 Code Review Checklist Questions from 394 Students. ICSE (SEET) 2021: 20-29 - [c19]Chakkrit Kla Tantithamthavorn, Jirayus Jiarpakdee:
Explainable AI for Software Engineering. ASE 2021: 1-2 - [c18]Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, Patanamon Thongtanunam:
PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. ASE 2021: 407-418 - [c17]Chanathip Pornprasit, Chakkrit Tantithamthavorn:
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction. MSR 2021: 369-379 - [c16]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, John C. Grundy:
Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models. MSR 2021: 432-443 - [i14]Chun Yong Chong, Patanamon Thongtanunam, Chakkrit Tantithamthavorn:
Assessing the Students' Understanding and their Mistakes in Code Review Checklists - An Experience Report of 1, 791 Code Review Checklist Questions from 394 Students. CoRR abs/2101.04837 (2021) - [i13]Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John C. Grundy, Wray L. Buntine:
SQAPlanner: Generating Data-Informed Software Quality Improvement Plans. CoRR abs/2102.09687 (2021) - [i12]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, John C. Grundy:
Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models. CoRR abs/2102.12007 (2021) - [i11]Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu:
Deep Learning for Android Malware Defenses: a Systematic Literature Review. CoRR abs/2103.05292 (2021) - [i10]Chanathip Pornprasit, Chakkrit Tantithamthavorn:
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction. CoRR abs/2103.07068 (2021) - 2020
- [j7]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude:
The impact of automated feature selection techniques on the interpretation of defect models. Empir. Softw. Eng. 25(5): 3590-3638 (2020) - [j6]Chakkrit Tantithamthavorn, Ahmed E. Hassan, Kenichi Matsumoto:
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models. IEEE Trans. Software Eng. 46(11): 1200-1219 (2020) - [c15]Chaiyakarn Khanan, Worawit Luewichana, Krissakorn Pruktharathikoon, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Morakot Choetkiertikul, Chaiyong Ragkhitwetsagul, Thanwadee Sunetnanta:
JITBot: An Explainable Just-In-Time Defect Prediction Bot. ASE 2020: 1336-1339 - [c14]Wisam Haitham Abbood Al-Zubaidi, Patanamon Thongtanunam, Hoa Khanh Dam, Chakkrit Tantithamthavorn, Aditya Ghose:
Workload-aware reviewer recommendation using a multi-objective search-based approach. PROMISE 2020: 21-30 - [i9]Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, Kenichi Matsumoto:
Predicting Defective Lines Using a Model-Agnostic Technique. CoRR abs/2009.03612 (2020) - [i8]Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John C. Grundy:
Explainable AI for Software Engineering. CoRR abs/2012.01614 (2020)
2010 – 2019
- 2019
- [j5]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto:
The Impact of Automated Parameter Optimization on Defect Prediction Models. IEEE Trans. Software Eng. 45(7): 683-711 (2019) - [c13]Suraj Yatish, Jirayus Jiarpakdee, Patanamon Thongtanunam, Chakkrit Tantithamthavorn:
Mining software defects: should we consider affected releases? ICSE 2019: 654-665 - 2018
- [j4]Safwat Hassan, Chakkrit Tantithamthavorn, Cor-Paul Bezemer, Ahmed E. Hassan:
Studying the dialogue between users and developers of free apps in the Google Play Store. Empir. Softw. Eng. 23(3): 1275-1312 (2018) - [j3]Chakkrit Tantithamthavorn, Surafel Lemma Abebe, Ahmed E. Hassan, Akinori Ihara, Kenichi Matsumoto:
The impact of IR-based classifier configuration on the performance and the effort of method-level bug localization. Inf. Softw. Technol. 102: 160-174 (2018) - [c12]Safwat Hassan, Chakkrit Tantithamthavorn, Cor-Paul Bezemer, Ahmed E. Hassan:
Studying the dialogue between users and developers of free apps in the google play store. ICSE 2018: 164 - [c11]Chakkrit Tantithamthavorn, Ahmed E. Hassan:
An experience report on defect modelling in practice: pitfalls and challenges. ICSE (SEIP) 2018: 286-295 - [c10]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude:
AutoSpearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Models. ICSME 2018: 92-103 - [c9]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude:
Artefact: An R Implementation of the AutoSpearman Function. ICSME 2018: 711 - [i7]Chakkrit Tantithamthavorn, Ahmed E. Hassan, Kenichi Matsumoto:
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models. CoRR abs/1801.10269 (2018) - [i6]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto:
The Impact of Automated Parameter Optimization on Defect Prediction Models. CoRR abs/1801.10270 (2018) - [i5]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Ahmed E. Hassan:
The Impact of Correlated Metrics on Defect Models. CoRR abs/1801.10271 (2018) - [i4]Chakkrit Tantithamthavorn, Surafel Lemma Abebe, Ahmed E. Hassan, Akinori Ihara, Kenichi Matsumoto:
The Impact of IR-based Classifier Configuration on the Performance and the Effort of Method-Level Bug Localization. CoRR abs/1806.07727 (2018) - [i3]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude:
AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models. CoRR abs/1806.09791 (2018) - 2017
- [j2]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto:
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models. IEEE Trans. Software Eng. 43(1): 1-18 (2017) - 2016
- [j1]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto:
Comments on "Researcher Bias: The Use of Machine Learning in Software Defect Prediction". IEEE Trans. Software Eng. 42(11): 1092-1094 (2016) - [c8]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto:
Automated parameter optimization of classification techniques for defect prediction models. ICSE 2016: 321-332 - [c7]Chakkrit Tantithamthavorn:
Towards a better understanding of the impact of experimental components on defect prediction modelling. ICSE (Companion Volume) 2016: 867-870 - [c6]Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Akinori Ihara, Kenichi Matsumoto:
A Study of Redundant Metrics in Defect Prediction Datasets. ISSRE Workshops 2016: 51-52 - [i2]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Ken-ichi Matsumoto:
Comments on "Researcher bias: The use of machine learning in software defect prediction". PeerJ Prepr. 4: e1260 (2016) - 2015
- [c5]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Akinori Ihara, Ken-ichi Matsumoto:
The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models. ICSE (1) 2015: 812-823 - [c4]Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Raula Gaikovina Kula, Norihiro Yoshida, Hajimu Iida, Ken-ichi Matsumoto:
Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review. SANER 2015: 141-150 - [i1]Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Ken-ichi Matsumoto:
Comments on "Researcher bias: The use of machine learning in software defect prediction". PeerJ Prepr. 3: e1260 (2015) - 2014
- [c3]Chakkrit Tantithamthavorn, Akinori Ihara, Hideaki Hata, Kenichi Matsumoto:
Impact Analysis of Granularity Levels on Feature Location Technique. APRES 2014: 135-149 - 2013
- [c2]Chakkrit Tantithamthavorn, Rattamont Teekavanich, Akinori Ihara, Ken-ichi Matsumoto:
Mining A change history to quickly identify bug locations : A case study of the Eclipse project. ISSRE (Supplemental Proceedings) 2013: 108-113 - [c1]Chakkrit Tantithamthavorn, Akinori Ihara, Ken-ichi Matsumoto:
Using Co-change Histories to Improve Bug Localization Performance. SNPD 2013: 543-548
Coauthor Index
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OpenAlex data
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last updated on 2024-10-07 21:22 CEST by the dblp team
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