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Open research questions in Software Testing and Debugging Techniques

188 gap statements mined from Software Testing and Debugging Techniques papers in our 7.9M-paper local library, which holds 1,526 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Further research can be conducted to improve the performance of RTM. The application of RTM to other domains can be explored.

    Requirements Coverage-Guided Minimization for Natural Language Test Cases · 2026 · DOI

  • The lack of semantic completeness in the simplified Z3 example. The manual and solver-dependent workflow required for the solver-based phase.

    Formal Verification of Transcompiled Mobile Applications Using First-Order Logic · 2025 · DOI

  • Failed continuations show a pattern we call acceptance bias: agents treat the inherited work as the definition of what remains to be done instead of checking it against the request.

    SWE-Residual: Evaluating Starting-State Robustness of Coding Agents · 2026

  • To achieve broad coverage, it organizes generation along risk categories, attack strategies, attack stages, and attack surfaces, and uses experience-guided exploration to steer generation toward under-explored regions.

    AutoRedClaw: Automated Red-Teaming for Claw-Style Agents · 2026

  • The lack of effective test programs and corresponding oracles for silent bug detection. The limitations of existing DL fuzzing techniques in detecting silent bugs.

    LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer · 2026 · DOI

  • TransFuzz's bug transfer mechanism relies on API similarity matching to select target APIs for test migration, but the paper lacks analysis of which bug categories transfer successfully across API boundaries versus those that fail (code migration failures). Characterizing the transferability landscape of different silent bug types in deep learning libraries would enable more targeted bug transfer strategies.

    LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer · 2026 · DOI

  • Existing automated test generation tools have limitations in terms of branch coverage and bug detection. Large Language Models have not been fully explored for automated software test generation.

    Large Language Models for Automated Software Test Generation · 2026 · DOI

  • Predictive defect analysis and intelligent automation frameworks can be explored in future research.

    Defect Leakage Analysis in Software Testing · 2026 · DOI

  • The study identifies the need for effective testing strategies to reduce defect leakage.

    Defect Leakage Analysis in Software Testing · 2026 · DOI

  • ProcCtrlBench has several limitations, including an ontology that is not exhaustive. Some defect classes are only partially observable from execution traces. The current instantiation depends on process-level annotation, and the scale of this annotation remains limited.

    ProcBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents · 2026

  • Additional validation is needed before extending strong conclusions to other agent domains. The current study focuses on coding agents, and future research should explore the applicability of ProcBench to other domains.

    ProcBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents · 2026

  • The generalizability of our approach is limited by the dataset’s scope, which consists of 50 real-world ESCMs from industry sources.

    Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems · 2026

  • Developing reduction metrics that better reflect human debugging effort is an open problem in the field, and one to which we believe DRReduce could contribute, since its dependency-graph-based design enables structurally-aware manipulation of the reduced program rather than only size-based optimization.

    DRReduce: Enhancing Syntax-Guided Program Reduction with Dependency Reconstruction · 2026

  • The weights used in the scoring system are policy defaults, not measured quantities. The system was tested in a limited setting, with two working instantiations.

    One Gate, Aimed Twice: A Reusable Pattern for Deterministic AI Governance · 2026 · DOI

  • EvoSuite's modular testing mode is ineffective in practice. There is a need to improve modular testing for object-oriented programs in Java.

    On the Effectiveness of Modular Testing in EvoSuite · 2026 · DOI

  • ComPass is built on top of the pre-trained model BERT by a self-supervised contrastive objective, thus learning code representation from large-scale unlabeled code snippets. ComPass then utilizes code transformation rules to generate semantic-preserving patches, which are used to fine-tune BERT with a binary classifier jointly.

    ComPass: Contrastive Learning for Automated Patch Correctness Assessment in Program Repair · 2026 · DOI

  • Investigating the application of SA-SBT to other types of robot controllers - Examining the transferability of the proposed approach to real-world scenarios - Comparing the performance of the proposed approach with other SA-SBT…

    Surrogate-Assisted Search-Based Testing of Robot Controllers Based on Enzymatic Numerical P Systems · 2026 · DOI

  • Existing SA-SBT techniques predominantly employ black-box surrogates evaluated on short, static scenarios. They rarely exploit internal controller specifications or assess transferability to reactive closed-loop environments.

    Surrogate-Assisted Search-Based Testing of Robot Controllers Based on Enzymatic Numerical P Systems · 2026 · DOI

  • A Lack of Semantic Completeness: The simplified Z3 example does not encode certain essential semantic aspects, - Manual and Solver-Dependent Workflow: Both the source and target programs had to be manually rewritten as logical formulas and submitted into the solver, - No Direct Link to Programming Languages: Although Z3 can check for satisfiability, it cannot parse or interpret Swift or Java…

    Formal Verification of Transcompiled Mobile Applications Using First-Order Logic · 2025 · DOI

  • Generalization to other languages and domains. Investigation of the cost-effectiveness of using Large Language Models for automated software test generation.

    Large Language Models for Automated Software Test Generation · 2026 · DOI

  • These findings are scoped to competitive programming (ICPC World Finals + Codeforces), which provides rigorous, binary-correctness evaluation; generalization to open-ended software engineering tasks warrants further investigation…

    A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback · 2026

  • Further development of the MTR framework to handle more complex models. Investigation of the use of the framework in different industries.

    MTR framework for teaching model-based testing · 2026 · DOI

  • Future work: a combined verifier; detectors for value-level grain and mild data-quality faults; in-the-wild LLM-generated transformations; and broader pipeline and dialect coverage.

    Verifying LLM-Generated Data-Pipeline Transformations: A Hybrid Static–Semantic Approach for Leakage and Data-Quality Faults · 2026 · DOI

  • Applying the proposed governance pattern to other domains and workflows. Investigating the use of measured quantities instead of policy defaults in the scoring system.

    One Gate, Aimed Twice: A Reusable Pattern for Deterministic AI Governance · 2026 · DOI

  • Investigating the application of transformer models to test case generation. Developing more efficient and effective NLP-based methods.

    Comparative analysis of natural language processing methods for automated test case generation · 2026 · DOI

Most-cited papers in Software Testing and Debugging Techniques

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Open questions raised by papers listed on this page include: (1) The lack of effective test programs and corresponding oracles for silent bug detection (“LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and…”, 2026); (2) Large Language Models have not been fully explored for automated software test generation (“Large Language Models for Automated Software Test Generation”, 2026); (3) There is a need to improve modular testing for object-oriented programs in Java (“On the Effectiveness of Modular Testing in EvoSuite”, 2026); (4) The current instantiation depends on process-level annotation, and the scale of this annotation remains limited (“ProcBench: Evaluating Process-Level Defects and Control Preservation in LLM…”, 2026); (5) The lack of semantic completeness in the simplified Z3 example (“Formal Verification of Transcompiled Mobile Applications Using First-Order Logic”, 2025). 188 gap statements have been mined from Software Testing and Debugging Techniques papers in our 7.9M-paper local library, which holds 1,526 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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