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arxiv: 2605.17374 · v1 · pith:F6GG7NKQnew · submitted 2026-05-17 · 💻 cs.SE

Towards an Ontology for the Foundations of Software Languages

Ralf L\"ammel This is my paper

Pith reviewed 2026-05-19 23:29 UTC · model grok-4.3

classification 💻 cs.SE
keywords ontologysoftware languagesprogramming languagesmodeling languagescomputer science educationgenerative AIknowledge resource
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The pith

A new ontology called Foundations of Software Languages organizes core concepts across programming, modeling, and software engineering languages to aid computer science education.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the first release of the Foundations of Software Languages ontology, or FSL. This resource categorizes language types and concepts, associated tools and methods, underlying formal systems, and how languages fit into broader software engineering work. It was assembled through standard ontology steps of setting expectations, reusing prior work, conceptualizing, formalizing, and validating, with generative AI helping on discovery, classification, linkage, completion, and transformation. The central aim is to create a connected knowledge base that ties together separate areas of computer science for teaching purposes.

Core claim

The FSL ontology organizes the foundations underlying software languages, which include programming languages, modeling languages, and other types used in software engineering. It covers language categories, language concepts, associated tools and methodological approaches, the formal systems or other formal entities underlying software languages, and the embedding of software languages into software engineering activities. The ontology was built through a standard methodology of expectations, reuse, conceptualization, formalization, and validation, with generative AI support for discovery, classification, linkage, completion, and transformation.

What carries the argument

The FSL ontology, a structured knowledge representation that categorizes and links foundations of software languages for educational use.

Load-bearing premise

The foundations of software languages can be usefully captured, categorized, and formalized into a single ontology using standard engineering steps plus generative AI assistance, and that this structure will meaningfully aid education.

What would settle it

A controlled study in which students using the FSL ontology show no measurable improvement in connecting concepts across programming languages, modeling languages, and software engineering compared to a control group without it.

Figures

Figures reproduced from arXiv: 2605.17374 by Ralf L\"ammel.

Figure 1
Figure 1. Figure 1: Selected software language categories in FSL 1.2. In Need of an Ontology for the Foundations of Software Languages As a field matures and grows, it is natural to see the need for robust knowledge resources. SLE is no different in this respect, and such resources are perhaps even more important for SLE, as it is an interdisciplinary and knowledge-integrating field. In some 2 https://dblp.org/db/conf/sle/ [… view at source ↗
Figure 2
Figure 2. Figure 2: Selected software language concepts in FSL In our first scenario, we expect to be able to query different classes (types) of software languages with the leaves of the shown classification hierarchy corresponding to actual lan- [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FSL entity types — top-level view ment with current trends in ontology engineering, we leverage GenAI to semi-automatically perform some steps of ontology engineering. We provide explained traces of GenAI usage. 1.5. Roadmap of the paper Sec. 2 develops the ‘Materials and Methods’ of our research. At its heart, the section outlines nine phases of bringing FSL into existence. There is also a part on the use… view at source ↗
Figure 4
Figure 4. Figure 4: Selected formal entities in FSL and their associations among themselves and with languages and tools 2. Materials and Methods Sec. 2.1 reviews the method of ontology engineering and explains how it is applied in the methodology for FSL. Sec. 2.2 focuses on an important aspect of ontology engineering, i.e., reuse, to clarify what the knowledge resources underlying FSL are and to what extent existing foundat… view at source ↗
Figure 5
Figure 5. Figure 5: Selected SE activities in FSL(all related to implemented) and their associations with languages, tools, and artifacts the phases of developing FSL in terms of the (non-) goals for each stage. Finally, Sec. 2.4 explains the use of GenAI in this research and publication effort. 2.1. Ontology Engineering FSL — the key result of the paper — has been created by means of an ontology engineering methodology. Acco… view at source ↗
Figure 6
Figure 6. Figure 6: The technological space of Modelware in FSL [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Result of Phase 2 — Category Discovery Legend: rectangles for individuals; ellipses for categories. Added categories and individuals are highlighted to visualize the delta between this phase and the previous phase [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Result of Phase 3 — Taxonomy Enrichment [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Dependencies for FSL’s modules 3.7. Phase 7 — SL Concept Coverage In accordance with the goal Programming Paradigm Coverage (PPC) (Sec. 2.3.7), there are corresponding concepts included in FSL; refer back to [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Illustrative sets of issues fixed in Phase 9 — Review 4. Discussion We will now summarize our findings and suggest directions for future research. 4.1. Summary of findings We draw the following conclusions from the conducted research: An ontological definition of the software languages field Up to now, software languages and their engineering have been conceptualized only informally and pragmatically. FSL… view at source ↗
read the original abstract

The notion of software languages subsumes programming languages, modeling languages, and yet many other types of languages used in software engineering. The emerging ontology `Foundations of Software Languages' (FSL) organizes the foundations underlying software languages. We are concerned with language categories, language concepts, associated tools and methodological approaches, the formal systems or other formal entities underlying software languages, and the embedding of software languages into into software engineering activities. The primary objective of FSL is to serve as a knowledge resource in Computer Science education by connecting several subject areas in a principled manner. The first release of FSL (V1), as discussed in this paper, was built through a relatively standard methodology involving common steps for expectations, reuse, conceptualization, formalization, and validation. We leveraged GenAI to support ontology engineering (discovery, classification, linkage, completion, and transformation).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims to have developed the initial version of an ontology called Foundations of Software Languages (FSL) that structures the foundations of various software languages, including their categories, concepts, tools, formal underpinnings, and integration with software engineering. It follows a conventional ontology engineering methodology enhanced by generative AI and aims primarily to support Computer Science education by linking related subject areas.

Significance. Should the ontology be shown to be well-constructed and educationally effective, it would offer a significant contribution by providing a unified knowledge base for software languages that spans multiple CS disciplines. The integration of GenAI in the engineering process is a notable strength that could inspire similar work in knowledge representation for software engineering.

major comments (2)
  1. [Abstract] The paper mentions performing validation as part of the methodology but does not report any specific results from this validation, such as inter-rater agreement for classifications or accuracy of GenAI suggestions, which is necessary to establish the reliability of the FSL ontology.
  2. [Abstract] No evidence or data is presented to show that the ontology achieves its stated primary objective of connecting subject areas in a way that benefits CS education, such as through student feedback or comparative learning assessments.
minor comments (1)
  1. [Abstract] Duplicate word 'into' in 'embedding of software languages into into software engineering activities'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the potential significance of the Foundations of Software Languages (FSL) ontology for unifying knowledge across computer science disciplines. We address each major comment point by point below, with proposed revisions to strengthen the presentation of the methodology and the educational objectives of this initial release (V1).

read point-by-point responses

  1. Referee: [Abstract] The paper mentions performing validation as part of the methodology but does not report any specific results from this validation, such as inter-rater agreement for classifications or accuracy of GenAI suggestions, which is necessary to establish the reliability of the FSL ontology.

    Authors: We appreciate the referee drawing attention to the need for greater transparency on validation. The manuscript describes a standard ontology engineering process that includes validation, which for FSL V1 consisted of expert-driven iterative review, cross-checking of categories and concepts, and manual assessment of GenAI-generated suggestions for relevance and accuracy. No formal inter-rater agreement statistics or quantitative accuracy metrics were computed in this initial release, as the emphasis was on establishing a coherent foundational structure rather than on statistical validation. In the revised manuscript we will expand the methodology section to provide concrete examples of the validation activities performed (e.g., how conflicting classifications were resolved and how GenAI outputs were filtered or corrected), thereby clarifying the reliability measures taken without overstating the quantitative rigor of V1. revision: partial

  2. Referee: [Abstract] No evidence or data is presented to show that the ontology achieves its stated primary objective of connecting subject areas in a way that benefits CS education, such as through student feedback or comparative learning assessments.

    Authors: The primary objective of FSL is to function as a knowledge resource that connects subject areas for educational purposes. Because the present paper reports the construction and content of the first release (V1), it does not yet include empirical studies of educational impact. Such studies (student feedback, controlled learning assessments, or curriculum integration trials) lie outside the scope of an ontology-development paper and are planned for subsequent work once FSL is made available to educators. In the revision we will strengthen the discussion by adding explicit examples of cross-area linkages already present in FSL (e.g., between formal semantics, language paradigms, and software-engineering activities) and by outlining concrete scenarios for its use in CS curricula. This will better illustrate the intended educational value while respecting the current paper’s focus on ontology construction. revision: partial

Circularity Check

0 steps flagged

No circularity: constructive ontology assembly from domain knowledge

full rationale

The paper presents a standard ontology engineering methodology (expectations, reuse, conceptualization, formalization, validation) augmented by GenAI for discovery/classification/linkage/completion/transformation. No equations, predictions, or first-principles derivations are claimed; the work is explicitly descriptive and constructive, assembling FSL as a knowledge resource without reducing any result to its own inputs by definition or self-citation load-bearing. The primary objective of connecting subject areas for CS education is stated as aspirational intent rather than a derived claim that collapses into fitted parameters or prior self-work. This matches the most common honest non-finding for papers that document construction rather than inference.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central contribution is the new ontology itself. The work rests on the domain assumption that software languages share organizable foundations and that standard ontology methods plus GenAI can produce a useful educational resource. No numerical free parameters are described.

axioms (1)
  • domain assumption Software languages possess identifiable categories, concepts, tools, and formal underpinnings that can be captured in an ontology.
    This premise underpins the entire effort to build FSL as a knowledge resource.
invented entities (1)
  • Foundations of Software Languages (FSL) ontology no independent evidence
    purpose: To organize and connect foundations of software languages for computer science education.
    The ontology is the primary new artifact introduced by the paper.

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discussion (0)

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Reference graph

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