{"id":1474,"date":"2026-05-27T15:39:15","date_gmt":"2026-05-27T15:39:15","guid":{"rendered":"https:\/\/cisose.fit.ac.jp\/aitest\/?page_id=1474"},"modified":"2026-05-27T15:43:18","modified_gmt":"2026-05-27T15:43:18","slug":"accepted-papers-2","status":"publish","type":"page","link":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/accepted-papers-2\/","title":{"rendered":"Accepted Papers"},"content":{"rendered":"\t\t
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IEEE AITest 2026 received a record number of 110 submissions this year. Following a rigorous review and discussion process:<\/p>

  • 19 papers were accepted as Regular Papers<\/li>
  • 14 papers were accepted as Short Papers<\/li><\/ul>

    This corresponds to an overall acceptance rate of approximately 30.0%, with a regular paper acceptance rate of approximately 17.3%.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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    Regular Papers<\/h2>

    Koyama, R., Wang, Z., Karolita, D., Li, J., & Tei, K. Bridging the interpretation gap in accessibility testing: Empathetic and legal-aware bug report generation via large language models<\/em>.<\/p>

    Wang, T., Yuan, F., Miao, C., Seshadri Kaniyanoor, M., & Matsumoto, K. AutoQABench: A three-level UX benchmark for automated evaluation of open-ended LLM responses<\/em>.<\/p>

    Yamada, M., Kato, M., & Takahashi, J. Improving LLM-based unit test generation through root-cause-driven prompt design<\/em>.<\/p>

    Marimuthu, G. Metamorphic testing of multi-agent LLM systems: A trace-based behavioral oracle framework<\/em>.<\/p>

    Poola, R., Yakkali, L. V. S. G. V. G., & Dadi, E. R. N. ECG-based cardiac arrhythmia detection via adaptive continual active optimization with hybrid parallel ensemble architecture<\/em>.<\/p>

    Trevino, E. Deterministic behavioral contract testing for AI features at the browser layer<\/em>.<\/p>

    Nguyen, T. D., Oberreuter, G., Steckenbiller, S., Tkalcic, P., & Fraser, G. Evaluating DL test adequacy metrics: A correlation study with DeepCrime’s mutation score<\/em>.<\/p>

    Twabi, A., Ding, Y., & Kondo, T. Formal trajectory analysis for testing agentic AI in stateful environments<\/em>.<\/p>

    Lin, L.-J., & Hong, C.-D. Robustness verification of RNN with abstraction refinement<\/em>.<\/p>

    Shahzad, N., & Wotawa, F. Ontology-based testing of reinforcement learning applications<\/em>.<\/p>

    Lu, R. W., Chen, W. P., & Yu, F. Dual-track red teaming and evaluation for Traditional Chinese localized LLMs<\/em>.<\/p>

    Nhan, T. P. T., & Shibata, T. Transformer-based temporal action localization of fine-grained operations in biological experiment videos and analysis of compositional limitations<\/em>.<\/p>

    Zhang, L., Zhao, F., & Yang, X. A C-SSRS-based risk-aware evaluation framework for self-harm safety in LLMs<\/em>.<\/p>

    Kuang, F., Liu, D., Zhao, M., & Mi, L. A dynamic evaluation approach to repository-level code generation via LLM<\/em>.<\/p>

    Wang, Y., Chen, J., & Wang, Q. LLM-based detection and test generation for command injection vulnerabilities in Python<\/em>.<\/p>

    Mahmud, A., Rawajfih, Y., & Arnold, R. Beyond bypass: Measuring vulnerability regression in LLM-generated security patches<\/em>.<\/p>

    De Vita, G., Humbatova, N., & Tonella, P. Evaluating the efficacy and diversity of DNN test input prioritisation techniques<\/em>.<\/p>

    Manope, F. I., Faqih, A. R., Fadhlurrohman, D. H., Husen, J. H., & Riskiana, R. R. LLM-based automatic metamorphic test case generation for LLM fairness testing<\/em>.<\/p>

    Wang, M., Zhou, Z., Zhang, F., Zhao, J., & Truong, V. T. D. Improving robustness of semantic segmentation for autonomous driving: A case study<\/em>.<\/p>

    Short Papers<\/h2>

    Saad, O., Abdelkarim, M., & Eladawi, R. TE-TCP-Net: Parallel transformer-encoders for test case prioritization<\/em>.<\/p>

    Yamin, M. M. VectorSec: A web-based AI security scanner for systematic evaluation of LLM vulnerabilities<\/em>.<\/p>

    Domingues, J., Duarte, D., Sousa, A., & Pombo, N. AI4SE for CI\/CD: Explainable code smell risk analysis<\/em>.<\/p>

    Rakotoarison, L., & Randriamahenintsoa, F. Do AI agents exhibit greed or empathy in shared resource environments?<\/em><\/p>

    Molina, M., G\u00fcnther, A., & Liggesmeyer, P. Connecting uncertainty quantification to safety engineering: Uncertainty gate framework for safe ML decision making<\/em>.<\/p>

    Johnson, M., & Slhoub, K. Direct transfer learning for cross-project test case prioritization under cold-start conditions<\/em>.<\/p>

    Bulda, A., Itkin, A., Degtiarenko, D., & Itkin, I. Passive testing of AI-enhanced financial systems via protocol traffic analysis and property-based validation<\/em>.<\/p>

    Antony, S. SwarmMind: Emergent LLM ensemble consensus with penalise-only feedback for adaptive futures trading<\/em>.<\/p>

    Banik, D., Chowdhury, K., & Shamim, S. I. All smoke, no alarm: Oracle signals in agent-authored test code<\/em>.<\/p>

    Poola, R., Kusha, B. P., & Varri, P. C. R. Spatiotemporal graph neural networks with adaptive graph learning for public transit demand forecasting and load-aware route optimization<\/em>.<\/p>

    Pasupuleti, V., Songa, S. T., Gulkotwar, N., Allala, S. R., & Tyagi, S. Behavioral regression testing for continuously updated machine learning models<\/em>.<\/p>

    Ni, L., & Tao, L. Bandit-controlled semi-ensemble learning for accuracy-energy trade-off under real-time edge conditions<\/em>.<\/p>

    Blanco, R., Su\u00e1rez-Cabal, M. J., Tuya, J., & de la Riva \u00c1lvarez, C. A RAG approach for assisting testers in database query testing<\/em>.<\/p>

    Katikala, J. M., Sharma, K., Swaminathan Ravi Kumar, T., & Sharma, S. Conversational AI for safety-critical virtual and extended reality: Intelligent agents across health informatics, navigation, and urban sensing<\/em>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    IEEE AITest 2026 received a record number of 110 submissions this year. Following a rigorous review and discussion process: 19 papers were accepted as Regular Papers 14 papers were accepted as Short Papers This corresponds to an overall acceptance rate of approximately 30.0%, with a regular paper acceptance rate of approximately 17.3%. Regular Papers Koyama, […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1474","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/pages\/1474","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/comments?post=1474"}],"version-history":[{"count":7,"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/pages\/1474\/revisions"}],"predecessor-version":[{"id":1482,"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/pages\/1474\/revisions\/1482"}],"wp:attachment":[{"href":"https:\/\/cisose.fit.ac.jp\/aitest\/index.php\/wp-json\/wp\/v2\/media?parent=1474"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}