Choosing the Right AI for Your Code: A Smarter Approach

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Author: Denis Avetisyan

Researchers now have a new framework to navigate the complex landscape of artificial intelligence models for software engineering tasks.

The framework adapts established decision modeling-extending it with data collection to facilitate automated pipelines-and thereby enables systematic, evidence-based evaluation not only of AI models themselves, but also of their iterative variations and supporting libraries, acknowledging that all systems, even those built on algorithms, are subject to change and require ongoing assessment.
The framework adapts established decision modeling-extending it with data collection to facilitate automated pipelines-and thereby enables systematic, evidence-based evaluation not only of AI models themselves, but also of their iterative variations and supporting libraries, acknowledging that all systems, even those built on algorithms, are subject to change and require ongoing assessment.

ModelSelect leverages knowledge graphs and multi-criteria decision-making to deliver transparent, reproducible, and context-aware AI model selection support.

Selecting appropriate AI models for research software engineering remains challenging despite their rapid proliferation, often relying on fragmented knowledge and ad hoc decisions. This work, ‘Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering’, addresses this gap by presenting ModelSelect, a framework that leverages knowledge graphs and multi-criteria decision-making to deliver transparent, reproducible, and context-aware recommendations. Empirical validation across fifty real-world case studies demonstrates ModelSelect’s ability to align with expert reasoning and achieve high coverage in both model and library selection. Could this approach establish a new standard for rigorous, evidence-based decision support in the increasingly complex landscape of research software development?

The Evolving Landscape of AI Tools: A Researcher’s Predicament

The proliferation of artificial intelligence models and libraries presents a significant challenge for researchers seeking the best tools for their work. Once a manageable landscape, the field now boasts countless options, each with varying strengths, weaknesses, and specialized applications. This exponential growth creates a considerable burden, as evaluating and comparing these components requires substantial time and expertise. Researchers frequently find themselves overwhelmed by the sheer volume of choices, struggling to determine which model-or even which library-best aligns with the specific requirements of their project. The task extends beyond simply identifying high-performing algorithms; it necessitates a deep understanding of computational resources, data compatibility, and the nuances of each tool’s implementation, ultimately hindering efficient progress and potentially leading to suboptimal research outcomes.

Evaluating artificial intelligence components through conventional means presents significant hurdles for researchers. Often, assessments rely heavily on manual testing and benchmarking, a process that demands considerable time and resources, particularly given the rapid proliferation of new models and libraries. Furthermore, these evaluations are frequently subjective, shaped by the specific datasets and metrics chosen by the assessor, which may not generalize well to other research contexts. Crucially, traditional methods rarely offer a comprehensive overview of relevant features – encompassing not only performance metrics like accuracy and speed, but also considerations like computational cost, data requirements, interpretability, and potential biases – leaving researchers with an incomplete picture and increasing the risk of selecting suboptimal tools for their work.

The proliferation of artificial intelligence models, while promising transformative advancements, presents a significant impediment to research progress across diverse fields. This bottleneck isn’t simply about having too many choices; it’s that the effort required to identify and validate the appropriate AI component-be it for image recognition, natural language processing, or complex data analysis-diverts valuable time and resources from actual investigation. Researchers find themselves spending increasing portions of their projects on comparative testing and adaptation, rather than on generating new knowledge. Consequently, the rate of discovery is demonstrably slowed, and potentially groundbreaking innovations are delayed or never realized, impacting everything from medical breakthroughs and climate modeling to materials science and fundamental physics. This creates a ripple effect, hindering not only individual research groups but also the overall advancement of science and technology.

AI model selection research employs diverse methodologies encompassing decision-making domains, data sources and collection techniques, weighting approaches, and ranking strategies.
AI model selection research employs diverse methodologies encompassing decision-making domains, data sources and collection techniques, weighting approaches, and ranking strategies.

ModelSelect: Architecting a System for Intelligent Component Discovery

ModelSelect employs automated data pipelines to systematically gather and process information pertaining to both AI Models and AI Libraries from multiple, distributed sources. These pipelines function continuously, extracting metadata, performance metrics, and usage statistics. Data ingestion is not limited to a single repository; it actively monitors GitHub repositories, dedicated AI library databases, and quality assessment platforms. The collected data undergoes automated cleaning, normalization, and validation before being integrated into the system’s Knowledge Graph. This automated process ensures data currency and scalability, allowing ModelSelect to maintain a comprehensive and up-to-date catalog of available AI components without manual intervention.

The ModelSelect framework employs three primary data extraction pipelines to achieve comprehensive coverage of AI models and libraries. The GitHub Repository Data Extraction Pipeline gathers metadata and code-level information directly from relevant GitHub repositories, including commit history, licensing, and documentation. The AI Library Data Extraction Pipeline focuses on collecting details about published AI libraries, such as API specifications, dependencies, and supported functionalities. Finally, the Quality Assessment Data Extraction Pipeline aggregates metrics related to model performance, robustness, and fairness, sourced from benchmark datasets and evaluation reports. Data from these pipelines is then integrated into the ModelSelect Knowledge Graph.

The ModelSelect framework employs a Knowledge Graph to structure data regarding AI models and libraries. This graph represents AI components as nodes, with edges defining relationships such as dependencies, compatibility, and functional similarities. Node attributes detail component features, including performance metrics, licensing information, and input/output specifications. By formalizing these relationships, the Knowledge Graph facilitates intelligent reasoning; for example, identifying suitable model replacements based on functional equivalence or recommending libraries compatible with a specific modeling task. This structured representation is critical for automated decision-making and improved AI component discoverability within the system.

ModelSelect utilizes a knowledge base and inference model to facilitate informed decision-making.
ModelSelect utilizes a knowledge base and inference model to facilitate informed decision-making.

Deconstructing the Process: Data Acquisition and Feature Engineering

The Model-Feature Identification Pipeline utilizes Generative AI techniques to automatically extract relevant features directly from the textual descriptions and documentation associated with AI components. This process involves analyzing component specifications, API documentation, and accompanying explanatory text to identify key functionalities, input parameters, output characteristics, and performance metrics. The extracted features are then structured and normalized for use in subsequent model selection and evaluation processes, enabling a data-driven approach to understanding and comparing the capabilities of different AI components. This automated extraction reduces manual effort and improves the scalability of the model discovery framework.

The Quality Assessment Data Extraction Pipeline systematically evaluates AI libraries based on the characteristics defined in the ISO/IEC 25010 standard. This standard outlines ten quality characteristics – functionality, performance, reliability, usability, efficiency, maintainability, portability, security, compatibility, and conformance – each with sub-characteristics and metrics. The pipeline extracts data related to these characteristics from library documentation, code repositories, and testing reports. This process yields objective, quantifiable metrics for each library, allowing for consistent and comparable quality assessments. Data points include code complexity, test coverage, bug reports, documentation completeness, and resource utilization, all mapped to specific ISO/IEC 25010 sub-characteristics.

ModelSelect utilizes automated data extraction pipelines achieving a precision rate of 79% for Model/Feature Identification and 82% for data sourced from GitHub repositories. These precision rates are determined by evaluating the accuracy of extracted features against manually validated datasets. The high level of accuracy minimizes the inclusion of irrelevant or incorrect data, ensuring the quality of input for subsequent model selection and evaluation processes. This rigorous data quality control is critical for reliable and reproducible results within the framework.

Natural Language Querying (NLQ) within the framework enables researchers to articulate their desired model characteristics using plain language instead of formal query languages or complex filtering criteria. This functionality utilizes natural language processing techniques to interpret User Intent expressed as questions or statements – for example, “find models for image classification with high accuracy” – and translate them into actionable search parameters. By removing the need for specialized query expertise, NLQ broadens accessibility to the model discovery process and allows researchers to focus on defining their requirements rather than constructing complex queries. The system then leverages this interpreted intent to identify and retrieve relevant AI models from the available catalog.

ModelSelectPipelines streamlines data handling by integrating acquisition, enrichment, and mapping processes.
ModelSelectPipelines streamlines data handling by integrating acquisition, enrichment, and mapping processes.

Towards Efficient Selection: Ranking, Recommendation, and Impact

ModelSelect employs a Multi-Criteria Decision-Making (MCDM) approach, mirroring how humans evaluate complex choices. Rather than relying on a single performance metric, the system assesses AI components – models and libraries – against a variety of weighted factors, such as accuracy, efficiency, robustness, and community support. This allows for a nuanced ranking, acknowledging that the ‘best’ component isn’t always the one with the highest score on a single measure. The weighting system, customizable to specific research needs, ensures that the most important criteria heavily influence the final ranking. By aggregating these diverse factors, ModelSelect offers a comprehensive and adaptable method for identifying optimal AI components, moving beyond simplistic performance comparisons to provide a more holistic evaluation.

The proliferation of available AI models and libraries presents a significant challenge for researchers seeking optimal tools for specific tasks. The Top-k Selection method addresses this by efficiently narrowing the vast search space to a manageable subset of the k most relevant options. Rather than exhaustively evaluating every possibility, this approach prioritizes components based on pre-defined criteria and ranking algorithms, delivering a focused list for further investigation. This not only conserves computational resources but also accelerates the development process by enabling researchers to quickly identify and implement the most promising solutions, ultimately streamlining workflows and fostering innovation in the field of artificial intelligence.

ModelSelect demonstrates a substantial capacity for accurate AI component identification, achieving 86.96% coverage in model recommendations and 82.61% in library recommendations. This performance was validated through rigorous testing on a dataset comprised of practical, real-world case studies, ensuring the results reflect applicability beyond theoretical benchmarks. The high coverage rates suggest that ModelSelect effectively narrows the field of potential AI solutions, consistently surfacing relevant options for a given task. This capability is crucial for researchers and developers facing the increasingly complex landscape of available AI tools, as it minimizes wasted effort and facilitates efficient problem-solving.

ModelSelect streamlines the often-complex process of identifying suitable AI components, thereby liberating researchers from exhaustive manual searches and evaluations. This automation isn’t simply about convenience; it fundamentally alters the research workflow, allowing scientists to dedicate significantly more time and resources to the core intellectual challenges of their projects. By efficiently curating a selection of relevant models and libraries, ModelSelect diminishes the overhead associated with tool selection, fostering faster experimentation, iterative development, and ultimately, a notable acceleration in the pace of scientific discovery. The system effectively functions as a force multiplier, amplifying the impact of research efforts by removing logistical barriers and enabling a more focused pursuit of innovation.

The pursuit of optimal AI models, as detailed in ModelSelect, inevitably confronts the reality of systemic decay. Every model, regardless of initial promise, will eventually succumb to the pressures of evolving data and shifting requirements. This framework, with its emphasis on transparent decision-making and reproducible results, doesn’t prevent decay, but rather illuminates it. As Ken Thompson observed, “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code first, debug it twice.” This sentiment mirrors the core principle of ModelSelect: proactively acknowledging the inevitable need for model refinement and providing the tools to gracefully navigate that process. The system isn’t built for perfection, but for informed adaptation-a dialogue with the past, ensuring future iterations are built upon a foundation of clear, documented reasoning.

What Lies Ahead?

ModelSelect, as presented, addresses a current need-the rationalization of choice within a rapidly expanding solution space. Yet, the framework itself is subject to the same entropic forces it seeks to mitigate. The knowledge graph, however meticulously constructed, will inevitably decay as new models emerge and existing ones are superseded. Uptime for any particular configuration is merely temporary; the ‘best’ model is a fleeting designation.

Future iterations must acknowledge this impermanence. The focus should shift from identifying a singular ‘optimal’ model to understanding the rate of model obsolescence and building systems capable of adaptive recalibration. Stability is an illusion cached by time; the true metric is resilience – the capacity to gracefully degrade as the landscape shifts. The current emphasis on multi-criteria decision-making is sound, but requires continuous refinement of the criteria themselves.

Ultimately, the cost of decision-making – the latency incurred in evaluating alternatives – is the unavoidable tax every request must pay. Minimizing this latency demands not only algorithmic efficiency but a deeper understanding of the cognitive biases that influence model selection in the first place. The goal isn’t to eliminate uncertainty, but to account for it – to build systems that acknowledge their own limitations and evolve accordingly.

Original article: https://arxiv.org/pdf/2512.11984.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-16 22:34