Industry-Oriented Research Problems

• Industry-oriented research problems are challenges rooted in real-world pain points, realistic constraints, and actionable outcomes for practitioners.
• Methodologies like the Lean Research Inception framework and NaPiRE surveys rigorously quantify and validate these problems using empirical metrics and stakeholder engagement.
• Bridging academia and industry requires aligning research outputs with practical metrics, standardized frameworks, and continuous evaluation to ensure economic and operational impact.

Industry-oriented research problems are rigorously defined challenges whose solution is driven by verifiable industrial needs, operational constraints, and measurable business impact. Their formulation, prioritization, and assessment involve structured methodologies to bridge the persistent gap between academic advances and practical industrial requirements. Such problems are recognized as central to fostering research that is both scientifically novel and impactful in real-world contexts, especially in fields like cybersecurity, AI, requirements engineering (RE), software engineering (SE), robotics, and the industrial Internet of Things (IIoT).

1. Definitional Foundations and Core Attributes

An industry-oriented research problem is characterized by three principal dimensions: (i) grounding in real-world pain points, (ii) applicability within realistic constraints of practice, and (iii) actionable outcomes for practitioners. For software and systems engineering, these dimensions are instantiated as “valuable” (impact), “feasible” (doability under existing resources), and “applicable” (likelihood of adoption). This conceptual triad is the basis of the Lean Research Inception (LRI) framework, which operationalizes the assessment as: $R_i = \frac{v_i + f_i + a_i}{3}$ where $v_i, f_i, a_i$ are the (1–7 scale) ratings for value, feasibility, and applicability by participant $i$ (Pereira et al., 15 Jun 2025). The seven-attribute Problem Vision board formalizes these ideas further (practical problem, context, implications/impacts, practitioners, evidence, objective, research questions), ensuring that every phase of problem formulation is systematically transparent and stakeholder-aware (Pereira et al., 14 Dec 2025).

2. Methodologies for Identification, Formulation, and Assessment

Robust methodological frameworks have been developed to ensure that research problems authentically reflect industrial realities. The NaPiRE (Naming the Pain in Requirements Engineering) Initiative exemplifies this with bi-annual, globally replicated surveys capturing the taxonomy, frequency, severity, and root causes of RE pain points, including statistical confidence analysis: $$\text{FailureRatio}_i = \frac{N_{\text{failed},i}}{N_{\text{experienced},i}}$$ with precise Likert-based severity metrics ($\bar{S}_i$) and root-cause mapping (Fernández, 2017). LRI supplements such empirical studies with a five-phase workshop cycle, integrating practitioners directly into the creation and validation of the initial problem vision and subsequent assessment via 7-point semantic differentials and sub-dimension scoring (Pereira et al., 15 Jun 2025, Pereira et al., 14 Dec 2025).

In industrial AI, rigorous methodologies are prescribed: using open datasets and code (for reproducibility), deploying AutoML as a baseline, fixing success criteria a priori, explicitly modeling temporal drift, and routine statistical reporting over multiple runs and splits (Pfab et al., 17 Jun 2025).

3. Structural Gaps and Barriers Between Academia and Industry

Structural misalignments have been widely documented. The average academic development cycle ($T_{\text{dev}}^{\text{acad}}$) is far longer than industry’s ($T_{\text{dev}}^{\text{ind}}$), with priorities diverging in their metric vectors: $$\mathbf{M}^{\text{acad}} = \{\#\text{pubs},\ \text{impact factor},\ \text{grant funding}\},\quad \mathbf{M}^{\text{ind}} = \{ROI,\ \Delta C,\ \Delta T_{\text{deploy}}\}$$ Success in academia is defined by novelty and citation, while in industry, metrics such as ROI and time-to-market dominate (Kashef et al., 2023). Additionally, academic proposals can be hampered by a lack of industry-grade datasets, improperly scoped assumptions, insufficient cross-language capability (e.g., in LLM-driven SE), and disconnects regarding deployment, integration, and explainability expectations (Yu et al., 17 Dec 2025, Wan et al., 3 May 2024).

Barriers persist around data privacy and sharing: more than half of surveyed MSR researchers reported the inability to share proprietary data as a major impediment to collaboration (Sureka et al., 2015). In production environments, cultural and linguistic divides between domain and cybersecurity experts remain, hampering the discovery and robust articulation of joint research problems (Pennekamp et al., 2021).

4. Taxonomies and Case Studies: Industrial Pain Points and Research Thrusts

Comprehensive problem taxonomies and targeted case studies provide empirical grounding. In RE, primary industrial pain points include incomplete/hidden requirements (reported by 42% of organizations, with a 0.31 failure ratio), communication flaws (40%, 0.28), underspecified requirements, moving targets, and time-boxing. These are organized into artifact, communication, planning, and process categories, each substantiated by statistical data and causal diagrams (Fernández, 2017).

Industrial benchmarks, such as the Industrial Benchmark for RL, are engineered to reflect precisely the stochasticity, partial observability, high-dimensionality, delay, and multi-objective trade-offs found in real industrial control contexts. The IB environment incorporates explicit mathematical formulations of system dynamics, latent states, control-affecting parameters, and reward/cost functions, aligning RL research with the operational realities of production systems (Hein et al., 2017).

Case studies in robotics underscore joint priorities of precision, flexibility, and safety, leading to new methods in perception (CNN-based pose regression), contact-aware control (impedance controllers sequenced by haptic feedback), and human-in-the-loop scheduling and safety enforcement (Chen et al., 2022). AI research in manufacturing further exposes structural validation gaps when real-world success criteria and temporal drift are ignored, emphasizing the irreplaceable role of industry-defined, requirement-aware metrics (Pfab et al., 17 Jun 2025).

5. Best Practices and Frameworks for Industry–Academia Alignment

Formal strategies for bridging the gap include mapping academic deliverables to industry KPIs at project inception, embedding academic milestones in industry-standard stage-gate lifecycles, and directly incorporating time-to-publication into ROI models: $$ROI = \frac{\Delta\text{Value}(\text{deployable}) - \text{Cost}}{\text{Cost}}$$ Parallelization of development (co-authoring of specifications and prototypes), standardized documentation (e.g., GitHub, Confluence), and boundary-spanning personnel (co-op students, dual-experience HQP) foster mutual understanding. University-based incubators and accelerators serve as intermediaries, shepherding prototypes from proof-of-concept to validated products (Kashef et al., 2023).

The LRI/Problem Vision approach combines these with a structured problem board and Go/Pivot/Abort decision points determined by criterion means, ensuring continuous alignment throughout the research lifecycle (Pereira et al., 15 Jun 2025, Pereira et al., 14 Dec 2025).

6. Representative Industrial Domains and Research Problem Agendas

Domain-specific taxonomies provide actionable research directions. The PLANET4 taxonomy links process optimization and product innovation challenges (e.g., production monitoring, predictive maintenance, supply-chain logistics) to proven and emerging technologies (AI, IIoT, robotics, cloud, digital twins), enabling dynamic mapping of “need-to-technology” and identification of research gaps (Figliè et al., 2022).

In AI-based vulnerability management, industry-driven research problems focus on building high-throughput, per-class scalable detectors; modular, customizable models; cost-sensitive evaluation metrics; robust privacy-preserving data-sharing and training; and long-term industry–academia evaluation partnerships (Wan et al., 3 May 2024). In AI engineering, challenge areas include data quality management (drift, labeling, versioning), systematic process and traceability frameworks, deployment/compliance support, and safety-critical/real-time validation and explainability (Bosch et al., 2020).

SE research using LLMs highlights underexplored industrial needs: automated end-to-end requirements Q&A and synthesis; high-reliability, explainable AI-aided code tooling; cross-language and cross-ecosystem program analysis; evaluation procedures focused on edit distance and integration cost, not just functional metrics; and enforceable safeguards for code/data privacy and licensing (Yu et al., 17 Dec 2025).

7. Future Directions and Evaluation Metrics

Key open areas include federated and privacy-preserving data integration, industrial-scale dataset and artifact sharing, co-versioning of models and code/data, robust handling of drift and non-stationarity, and interpretable/ethical AI models suited for deployment in safety or security-sensitive settings (Li et al., 22 Jun 2024, Bosch et al., 2020).

Metrics for practical relevance, impact, and industrial applicability are increasingly codified in formal rubrics, such as: $R_{\mathrm{ind}(S)} = S + C + SC + M + A_R + A_P$ where terms denote (use of industrial Subjects, setting, Scale, mature Method, Addresses real problems, Practically implementable) (Garousi et al., 2018). Industry-oriented research problem identification thus demands multidimensional, iterative assessment—statistical, experiential, and collaborative—anchored in practitioner engagement, rigorous reporting, and lifecycle alignment from conception through deployment and measurement.