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REVIEW 2 major objections 5 minor 1 cited by

Taxonomic Trace Links: Rethinking Traceability and its Benefits

T0 review · 2 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper argues that trace links can be routed through a shared domain taxonomy instead of connecting artifacts directly, and reports a field trial that found a real design violation using such links.

desk verdict TTL's conceptual argument is solid and the study is honestly scoped, but the validation is too thin to carry the reliability and maintenance claims; still worth serious refereeing with expectations of major revision. read the letter →

arxiv 2504.20507 v1 pith:WSACGSFI submitted 2025-04-29 cs.SE

Waleed Abdeen , Michael Unterkalmsteiner , Alexandros Chirtoglou , Christoph Paul Schimanski , Heja Goli , Krzysztof Wnuk This is my paper · ORCID
classification cs.SE
keywords requirementstraceabilitytaxonomictracelinksdomaintaxonomyclassificationlinkmaintenanceverification3Ddesignmodelschallenges
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes that trace links do not have to connect artifacts directly: each artifact can be classified against a domain-specific taxonomy, and two artifacts are considered traced when they share a taxonomy class, or nearby classes. The argument is that this indirection fixes three practical failures of direct traceability: choosing the right granularity, coping with artifacts that have no common structure or tool, and deciding who owns the links. The paper backs this with a field study in an infrastructure railway project: 26 of 27 sampled natural-language requirements received at least one class from the SB11 taxonomy, objects in 3D design models already carried classes, and filtering by shared classes let the team verify the design against the requirements and find one real violation. The authors present the broader benefits as reasoned conjectures, not measured outcomes, and they list six implementation challenges to resolve next.

What carries the argument

The load-bearing device is the domain-specific taxonomy, a hierarchy of domain classes such as the SB11 construction-classification system with more than 2,000 nodes. Each artifact is multi-labeled with one or more classes, and tracing is a query: find artifacts whose labels equal, parent, child, sibling, or neighbor the source's labels. The taxonomy carries the argument in three ways: its parent-child structure supplies abstraction levels so granularity is chosen per query rather than per link; its fixed vocabulary gives heterogeneous tools a common interface; and splitting each trace into two artifact-to-taxonomy links assigns each artifact's creator responsibility for only their own artifact. Maintenance becomes local because a change to one artifact invalidates only its own taxonomy links, not links to every related artifact.

What would settle it

Take the same railway setting and have a second, independent team re-classify the 27 requirements and a random sample of 3D model objects, then measure inter-rater agreement on the SB11 codes. If agreement is low, or if two artifacts that share a code are frequently judged by domain experts to be unrelated, the core premise that shared taxonomy classes encode traceability fails. Conversely, an audit of all 30 codes across both subcontractors' models, rather than the 8 sampled, would show whether the inconsistencies observed are rare human slips or systematic.

Watch

Extended reading notes

Core claim

The central claim is that a domain taxonomy can act as a neutral intermediary for traceability: classify every artifact against the same hierarchy of domain concepts, and the taxonomy class becomes the trace link. In the validation, 27 requirements from a railway project were manually mapped to SB11 codes, and 26 were mappable to at least one code; the one failure came from a vague, context-dependent requirement. The study then checked the codes already applied to objects in two versions of 3D models from two subcontractors: all objects were coded, a handful of errors were found (typos, one misclassified road element, two inconsistent surface-water classifications), and only about 30% of individual objects could be followed between model versions because exported files lost object identifiers. Despite those limitations, tracing one requirement about fence placement to fence objects in the model revealed fences positioned in a prohibited zone, demonstrating the intended verification use case. The paper's conclusion is phrased as feasibility, not proof of benefit: it is feasible to implement TTL to trace requirements to 3D design models and verify those requirements.

Load-bearing premise

The whole approach stands on the premise that a domain taxonomy can be applied comprehensively and consistently by everyone who creates or edits an artifact, so that a shared code really means a shared concept; if codes are wrong or applied differently by different teams, the resulting trace links silently inherit those errors.

Editorial extensions

If this is right

  • If a requirement is split or changed, only the links from the requirement to the taxonomy need updating; links from source code and tests to the taxonomy stay valid, whereas direct links would all need rewriting.
  • Tracing multiple artifact types requires each artifact to carry taxonomy labels rather than links to every other artifact, so the number of links grows linearly with artifacts, not quadratically with artifact-type pairs.
  • Coverage and verification analyses can be done by filtering a repository by taxonomy class; the railway trial shows this can surface design non-conformances.
  • In regulated domains with existing taxonomies, new compliance sources only need to be classified against the same taxonomy to become traceable to all related artifacts.
  • Because each creator only needs to know their own artifact and the taxonomy, link creation can happen at artifact creation time instead of being deferred to a separate tracing role.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The strongest unstated test is classification reliability: the study sampled only 8 of 30 codes for cross-subcontractor consistency and could trace only about 30% of objects across versions, so a full audit of all codes could reveal silent inconsistencies that would undermine TTL's central premise.
  • In software engineering the bottleneck is likely the taxonomy itself: code, tests, and commits carry semantic names but no pre-existing classification system, so the approach's transfer depends on whether automatically built taxonomies reach the consistency that SB11 has from years of industry use.
  • A direct experiment could measure the claimed maintenance savings: subject the same requirement change to direct and TTL link maintenance and count link edits; the paper's example predicts TTL wins when more than two artifact types are involved.
  • The verification workflow is essentially a retrieval task, so its precision and recall could be compared against information-retrieval trace recovery on the same railway data; the paper reports finding one violation but does not quantify how many violations were missed.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper proposes Taxonomic Trace Links (TTL), an approach in which trace links between artifacts are established indirectly by classifying each artifact against a domain-specific taxonomy. It argues that this indirection addresses three traceability challenges: granularity of links, lack of common data structures, and unclear responsibility. The paper reports a field validation in a railway infrastructure project: 27 requirements were classified using the SB11 taxonomy, the SB11 codes of 3D model objects were checked for consistency, and one design nonconformity was found through TTL-based verification. The paper identifies six classification challenges and proposes a research roadmap for realizing TTL in practice.

Significance. The TTL concept is a plausible and potentially useful contribution to traceability research and practice, especially in domains where a taxonomy already exists. The validation study is a strength: it uses real industrial artifacts (Eastlink project), involves practitioners, and honestly reports the limitations of the reliability checks (e.g., only about 30% of objects were traceable between model versions). The paper is also careful to frame most benefit claims as conjectures. However, the empirical evidence is limited to a single project, 27 requirements, and one detected defect; the maintenance-benefit argument is based on a toy example; and the classification-consistency premise is only partially measured. The paper is openly presented as an initial validation, which makes the remaining gaps more acceptable, but the conclusion should be tempered to match the evidence.

major comments (2)
  1. [Section 5.2.2 / Section 7] The central feasibility claim rests on the premise that classification is comprehensive and consistent across artifacts, but the validation leaves the classification error rate largely unmeasured. Only about 30% of objects could be traced between model versions A1/A2 and B1/B2, and inter-subcontractor consistency was checked on 8 of 30 codes. The paper itself notes in Section 6.1 that if classification is inconsistent or incomplete, TTL links may be unreliable. Because a TTL link is a logical conjunction of two classifications joined by a shared code, any classification error silently produces false or missed links. I recommend softening the Section 7 claim from 'demonstrates that it is feasible' to 'provides initial evidence that it is feasible', or adding a quantitative reliability analysis (for example, a sample-size calculation with a target error-rate bound, or a sensitivity analysis on the untraceable 70% of objects).
  2. [Section 2.3] The maintenance-effort benefit is stated as a general result: 'when tracing more than two types of artifacts to each other, indirect trace link maintenance requires less effort than direct trace links.' The only support is a toy example with one requirement, two classes, and two test cases, and the effort arithmetic depends on specific assumptions (e.g., a single requirement is split into two). The claim is not derived from a cost model and may not hold when an artifact is associated with many taxonomy nodes or when the taxonomy itself changes. The paper should either present a more general cost argument or explicitly frame the example as illustrative rather than as a general finding.
minor comments (5)
  1. [Section 5.1 / Abstract] The paper states in Section 5.1 that it is not an evaluation study, yet the abstract refers to an 'initial validation with practitioners'; the distinction between validation (feasibility) and evaluation (utility) should be clarified in the introduction to set reader expectations.
  2. [Section 2.3] The claim that TTL creates 'up to twice as many links' is not defined in terms of what counts as a link; a precise definition (e.g., a binary association between an artifact and a taxonomy node) would make the maintenance comparison clearer.
  3. [Section 5.2.2] The text reads 'we found only two objects', but Figure 6 shows 'two apparent different objects'; please clarify whether these are two individual objects or two groups of objects.
  4. [Section 5.1] 'When we had conflicted opinions' should be 'when we had conflicting opinions'.
  5. [Section 5.2.1] A table summarizing the six classification challenges with examples and potential mitigations would improve readability and help the reader connect the challenges to the research roadmap.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the feasibility claim is grounded in an external validation study, and the paper explicitly frames broader benefits as conjecture.

full rationale

The paper's central empirical claim is that TTL can be implemented to trace requirements to 3D design models and to verify those requirements, supported by a field experiment using external project artifacts (Trafikverket/Eastlink requirements, HOCHTIEF design models, and the SB11 taxonomy) and practitioner workshops. No equation, fitted parameter, or analytical derivation is used to generate the results; the paper contains no predictive model whose output is equivalent to its input by construction. The self-citations ([11], [15], [49], [50]) introduce the TTL concept, an earlier recommender idea, a multi-label classification finding, and a taxonomy-quality compendium. These are contextual or motivational and do not function as a uniqueness theorem or as the sole justification for the feasibility conclusion. The limitations noted in Sections 5.2.2 and 5.4 (only about 30% of objects traceable between model versions, and 8 of 30 codes sampled for inter-rater consistency) weaken the empirical support for the classification-reliability premise, but they are validity threats rather than circular reasoning: the paper does not redefine reliability to match its data or rename a fitted value as a prediction. Therefore, under the required standard of exhibiting a specific reduction, no circular step is established.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The central claims rest on four domain assumptions listed above; no free parameters are fitted to data. The TTL construct is an invented conceptual entity with no independent external evidence yet.

assumptions (4)
  • domain assumption A domain-specific taxonomy with sufficient coverage either already exists or can be constructed for the target domain.
    The entire TTL mechanism depends on classifying artifacts against a taxonomy. Section 2.1 describes taxonomies as given; Section 6.2 acknowledges that in software engineering a taxonomy may need to be built, which is an unresolved prerequisite.
  • domain assumption Artifacts and design objects can be classified reliably and consistently using the taxonomy, and the classification itself constitutes the trace link.
    Section 2.2 equates classification with trace creation. Section 5.2.2 shows that only about 30% of objects could be traced between model versions and that a couple of classification errors existed, so reliability is only partially supported.
  • domain assumption The proposed benefits of indirection (abstraction, structure, and time) follow from the conceptual argument in Section 3.
    Each benefit is introduced with 'we conjecture'. No formal proof or controlled experiment demonstrates that TTL actually addresses granularity, interoperability, and responsibility better than direct links.
  • domain assumption Results from the infrastructure and systems engineering case transfer to software engineering contexts.
    Section 5.4 lists external validity as a threat and notes that software projects may lack established taxonomies and pre-classified artifacts. The paper offers arguments for transferability but no evidence.
invented entities (1)
  • Taxonomic trace link (TTL) implemented as an intermediate taxonomy link
    purpose: Acts as an indirect association between source and target artifacts to reduce maintenance effort and enable traceability analyses.
    TTL is the paper's core new construct. It has no falsifiable handle outside the paper itself; the only validation is the small case study reported here, and no external benchmark or prediction is provided.

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Cite this review

Pith. "Pith review of Taxonomic Trace Links: Rethinking Traceability and its Benefits." pith.science (2026). https://pith.science/paper/WSACGSFI

@misc{pith2026250420507,
  author       = {Pith},
  title        = {Pith review of: Taxonomic Trace Links: Rethinking Traceability and its Benefits},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WSACGSFI}},
  note         = {Machine review of arXiv:2504.20507}
}
read the original abstract

Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis. Despite its clear benefits, the \emph{practical} implementation of traceability poses significant challenges, leading to a reduced focus on the creation and maintenance of trace links. We propose a new approach -- Taxonomic Trace Links (TTL) -- which rethinks traceability and its benefits. With TTL, trace links are created indirectly through a domain-specific taxonomy, a simplified version of a domain model. TTL has the potential to address key traceability challenges, such as the granularity of trace links, the lack of a common data structure among software development artifacts, and unclear responsibility for traceability. We explain how TTL addresses these challenges and perform an initial validation with practitioners. We identified six challenges associated with TTL implementation that need to be addressed. Finally, we propose a research roadmap to further develop and evaluate the technical solution of TTL. TTL appears to be particularly feasible in practice where a domain taxonomy is already established

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

58 extracted references · 57 canonical work pages · cited by 1 Pith paper

  1. [1]

    In: Software and Systems Traceability, pp

    Gotel, O., Cleland-Huang, J., Hayes, J.H., Zisman, A., Egyed, A., Gr¨ unbacher, P., Dekhtyar, A., Antoniol, G., Maletic, J., M¨ ader, P.: Traceability fundamentals. In: Software and Systems Traceability, pp. 3–22. Springer, Berling, Heidelberg (2012)

    work page 2012

  2. [2]

    In: Doerr, J., Opdahl, A.L

    Bouillon, E., M¨ ader, P., Philippow, I.: A survey on usage scenarios for requirements traceability in practice. In: Doerr, J., Opdahl, A.L. (eds.) Requirements Engineering: Foun- dation for Software Quality. Lecture Notes in Computer Science, pp. 158–173. Springer, Berlin, Heidelberg (2013)

    work page 2013

  3. [3]

    M¨ ader, P., Egyed, A.: Do developers benefit from requirements traceability when evolving and maintaining a software system? Empir- ical Software Engineering 20(2), 413–441 (2015)

    work page 2015

  4. [4]

    Journal of Systems and Software 192, 111389 (2022)

    Fucci, D., Al´ egroth, E., Axelsson, T.: When traceability goes awry: An industrial experi- ence report. Journal of Systems and Software 192, 111389 (2022)

    work page 2022

  5. [5]

    Requirements Engineer- ing 27(1), 53–81 (2022)

    Maro, S., Stegh¨ ofer, J.-P., Bozzelli, P., Muc- cini, H.: TracIMo: a traceability introduction methodology and its evaluation in an agile development team. Requirements Engineer- ing 27(1), 53–81 (2022)

    work page 2022

  6. [6]

    Requirements Engineering (2023)

    Ruiz, M., Hu, J.Y., Dalpiaz, F.: Why don’t we trace? a study on the barriers to soft- ware traceability in practice. Requirements Engineering (2023)

    work page 2023

  7. [7]

    Requirements Engi- neering 29(2), 119–141 (2024)

    Mucha, J., Kaufmann, A., Riehle, D.: A sys- tematic literature review of pre-requirements specification traceability. Requirements Engi- neering 29(2), 119–141 (2024)

    work page 2024

  8. [8]

    In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp

    Wohlrab, R., Stegh¨ ofer, J.-P., Knauss, E., Maro, S., Anjorin, A.: Collaborative trace- ability management: Challenges and oppor- tunities. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 216–225. IEEE, Piscataway, NJ (2016). ISSN: 2332-6441

    work page 2016

Show all 58 references
  1. [9]

    Journal of Software: Evolution and Process 33(2), 2294 (2021)

    Charalampidou, S., Ampatzoglou, A., Karountzos, E., Avgeriou, P.: Empirical studies on software traceability: A mapping study. Journal of Software: Evolution and Process 33(2), 2294 (2021). Publisher: John Wiley & Sons, Ltd

    2021

  2. [10]

    In: 13th International Conference on Requirements Engineering, pp

    Arkley, P., Riddle, S.: Overcoming the trace- ability benefit problem. In: 13th International Conference on Requirements Engineering, pp. 385–389. IEEE, Piscataway, NJ (2005)

    2005

  3. [11]

    In: 2020 IEEE 28th International Requirements Engineer- ing Conference (RE), pp

    Unterkalmsteiner, M.: Early Requirements Traceability with Domain-Specific Tax- onomies - A Pilot Experiment. In: 2020 IEEE 28th International Requirements Engineer- ing Conference (RE), pp. 322–327. IEEE, Zurich, Switzerland (2020). ISSN: 2332-6441

    2020

  4. [12]

    Springer, Berling, Heidelberg (2014)

    Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engi- neering. Springer, Berling, Heidelberg (2014)

    2014

  5. [13]

    ACM Transactions on Software Engineering and Methodology (TOSEM) 28(2), 1–48 (2019)

    Wagner, S., Fern´ andez, D.M., Felderer, M., Vetr` o, A., Kalinowski, M., Wieringa, R., Pfahl, D., Conte, T., Christiansson, M.-T., Greer, D., et al.: Status quo in requirements engineering: A theory and a global family of surveys. ACM Transactions on Software Engineering and ...

    2019

  6. [14]

    Addison-Wesley Professional, Boston, MA (2004)

    Evans, E.: Domain-driven Design: Tack- ling Complexity in the Heart of Software. Addison-Wesley Professional, Boston, MA (2004)

    2004

  7. [15]

    In: 2020 16 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engi- neering (AIRE), pp

    Unterkalmsteiner, M.: TT-RecS: The Taxo- nomic Trace Recommender System. In: 2020 16 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engi- neering (AIRE), pp. 18–21. IEEE, Zurich, Switzerland (2020)

    2020

  8. [16]

    Empirical Software Engineering 19(6), 1565–1616 (2014)

    Borg, M., Runeson, P., Ard¨ o, A.: Recover- ing from a decade: a systematic mapping of information retrieval approaches to software traceability. Empirical Software Engineering 19(6), 1565–1616 (2014)

    2014

  9. [17]

    Information and Software Technology 54(7), 663–685 (2012)

    Portillo-Rodr´ ıguez, J., Vizca´ ıno, A., Piat- tini, M., Beecham, S.: Tools used in global software engineering: A systematic mapping review. Information and Software Technology 54(7), 663–685 (2012)

    2012

  10. [18]

    IEEE Trans- actions on Software Engineering 44(11), 1024–1038 (2017)

    Kim, M., Zimmermann, T., DeLine, R., Begel, A.: Data scientists in software teams: State of the art and challenges. IEEE Trans- actions on Software Engineering 44(11), 1024–1038 (2017)

    2017

  11. [19]

    Information and Software Technology72, 137–150 (2016)

    Neiva, F.W., David, J.M.N., Braga, R., Cam- pos, F.: Towards pragmatic interoperability to support collaboration: A systematic review and mapping of the literature. Information and Software Technology72, 137–150 (2016)

    2016

  12. [20]

    Information and Software Technology 70, 204–219 (2016)

    Bjarnason, E., Smolander, K., Engstr¨ om, E., Runeson, P.: A theory of distances in soft- ware engineering. Information and Software Technology 70, 204–219 (2016)

    2016

  13. [21]

    In: 9th International Conference on Quality Software, pp

    Dzung, D.V., Ohnishi, A.: Improvement of Quality of Software Requirements with Requirements Ontology. In: 9th International Conference on Quality Software, pp. 284–289. IEEE, Piscataway, NJ (2009)

    2009

  14. [22]

    In: 4th International Conference on Semantic Computing, pp

    Kof, L., Gacitua, R., Rouncefield, M., Sawyer, P.: Ontology and Model Alignment as a Means for Requirements Validation. In: 4th International Conference on Semantic Computing, pp. 46–51. IEEE, Piscataway, NJ (2010)

    2010

  15. [23]

    In: 23rd International Con- ference on Advanced Information Systems Engineering, pp

    Moser, T., Winkler, D., Heindl, M., Biffl, S.: Requirements Management with Seman- tic Technology: An Empirical Study on Automated Requirements Categorization and Conflict Analysis. In: 23rd International Con- ference on Advanced Information Systems Engineering, pp. 3–17. Spri...

    2011

  16. [24]

    Computers & Security 124, 102985 (2023)

    Mubarkoot, M., Altmann, J., Rasti-Barzoki, M., Egger, B., Lee, H.: Software compliance requirements, factors, and policies: A system- atic literature review. Computers & Security 124, 102985 (2023)

    2023

  17. [25]

    In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp

    Moy´ on, F., Almeida, P., Riofr´ ıo, D., Mendez, D., Kalinowski, M.: Security compliance in agile software development: A systematic mapping study. In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 413–420 (2020)

    2020

  18. [26]

    IEEE Software 41(4), 134–142 (2024)

    Santilli, T., Pelliccione, P., Wohlrab, R., Shahrokni, A.: Continuous compliance in the automotive industry. IEEE Software 41(4), 134–142 (2024)

    2024

  19. [27]

    (eds.) Software Process Improve- ment and Capability Determination vol

    Regan, G., McCaffery, F., McDaid, K., Flood, D.: Traceability-why do it? In: Mas, A., Mesquida, A., Rout, T., O’Connor, R.V., Dorling, A. (eds.) Software Process Improve- ment and Capability Determination vol. 290, pp. 161–172. Springer, Berlin, Heidel- berg (2012). Series Tit...

    2012

  20. [28]

    In: Proceedings of the 30th Annual ACM Symposium on Applied Computing

    Demuth, A., Riedl-Ehrenleitner, M., N¨ ohrer, A., Hehenberger, P., Zeman, K., Egyed, A.: DesignSpace: an infrastructure for multi- user/multi-tool engineering. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing. SAC ’15, pp. 1486–1491. Association for Comput...

    2015

  21. [29]

    In: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

    Neˇ si´ c, D., El-Khoury, J., Westman, J., Nyberg, M.: Building a web-based federated toolchain: Lessons learned from a four-year industrial project. In: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services. iiWAS201...

    2020

  22. [30]

    In: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Tech- nology

    Wieringa, R.: Design science as nested prob- lem solving. In: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Tech- nology. DESRIST ’09, pp. 1–12. Association for Computing Machinery, New York, NY, USA (2009)

    2009

  23. [31]

    In: International Work- ing Conference on Requirements Engineering: Foundation for Software Quality, pp

    Bouillon, E., M¨ ader, P., Philippow, I.: A sur- vey on usage scenarios for requirements trace- ability in practice. In: International Work- ing Conference on Requirements Engineering: Foundation for Software Quality, pp. 158–

  24. [32]

    Innovations in Systems and Software Engineering 10(4), 235–241 (2014)

    Kassab, M., Neill, C., Laplante, P.: State of practice in requirements engineering: con- temporary data. Innovations in Systems and Software Engineering 10(4), 235–241 (2014)

    2014

  25. [33]

    Cleland-Huang, J., Gotel, O., Zisman, A., et al.: Software and Systems Traceability vol. 2. Springer, Berlin, Heidelberg (2012)

    2012

  26. [34]

    In: 2009 ICSE Workshop on Traceability in Emerging Forms of Software Engineering, pp

    McMillan, C., Poshyvanyk, D., Revelle, M.: Combining textual and structural analysis of software artifacts for traceability link recov- ery. In: 2009 ICSE Workshop on Traceability in Emerging Forms of Software Engineering, pp. 41–48 (2009). ISSN: 2157-2194

    2009

  27. [35]

    In: 2010 Asia Pacific Software Engineering Conference, pp

    Hayashi, S., Yoshikawa, T., Saeki, M.: Sentence-to-Code Traceability Recovery with Domain Ontologies. In: 2010 Asia Pacific Software Engineering Conference, pp. 385– 394 (2010). ISSN: 1530-1362

    2010

  28. [36]

    In: 2013 7th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), pp

    Bavota, G., De Lucia, A., Oliveto, R., Panichella, A., Ricci, F., Tortora, G.: The role of artefact corpus in LSI-based trace- ability recovery. In: 2013 7th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), pp. 83–89 (2013). ISSN: 2157-2194

    2013

  29. [37]

    In: Proceedings of the 10th European Software Engineering Conference Held Jointly with 13th ACM SIG- SOFT International Symposium on Founda- tions of Software Engineering

    Heindl, M., Biffl, S.: A case study on value- based requirements tracing. In: Proceedings of the 10th European Software Engineering Conference Held Jointly with 13th ACM SIG- SOFT International Symposium on Founda- tions of Software Engineering. ESEC/FSE- 13, pp. 60–69. Associ...

    2005

  30. [38]

    In: 2009 ICSE Workshop on Aspect- Oriented Requirements Engineering and Architecture Design, pp

    Khan, S.S., Lock, S.: Concern tracing and change impact analysis: An exploratory study. In: 2009 ICSE Workshop on Aspect- Oriented Requirements Engineering and Architecture Design, pp. 44–48 (2009)

    2009

  31. [39]

    In: 2009 Agile Conference, pp

    Ghanam, Y., Maurer, F.: Extreme Product Line Engineering: Managing Variability and Traceability via Executable Specifications. In: 2009 Agile Conference, pp. 41–48 (2009)

    2009

  32. [40]

    Requirements Engineering 22(1), 41– 75 (2017)

    Buchmann, R.A., Karagiannis, D.: Modelling mobile app requirements for semantic trace- ability. Requirements Engineering 22(1), 41– 75 (2017)

    2017

  33. [41]

    In: 20th IEEE International Conference on Soft- ware Maintenance, 2004

    De Lucia, A., Fasano, F., Oliveto, R., Tor- tora, G.: Enhancing an artefact management system with traceability recovery features. In: 20th IEEE International Conference on Soft- ware Maintenance, 2004. Proceedings., pp. 306–315 (2004). ISSN: 1063-6773

    2004

  34. [42]

    In: 14th IEEE International Requirements Engi- neering Conference (RE’06), pp

    Lin, J., Lin, C.C., Cleland-Huang, J., Set- timi, R., Amaya, J., Bedford, G., Beren- bach, B., Ben Khadra, O., Duan, C., Zou, X.: Poirot: A Distributed Tool Supporting Enterprise-Wide Automated Traceability. In: 14th IEEE International Requirements Engi- neering Conference (RE...

    2006

  35. [43]

    In: 2011 27th IEEE Interna- tional Conference on Software Maintenance (ICSM), pp

    Gethers, M., Oliveto, R., Poshyvanyk, D., Lucia, A.D.: On integrating orthogonal infor- mation retrieval methods to improve trace- ability recovery. In: 2011 27th IEEE Interna- tional Conference on Software Maintenance (ICSM), pp. 133–142 (2011). ISSN: 1063- 6773

    2011

  36. [44]

    https://www.nlm.nih.gov/ mesh/meshhome.html Accessed 2025-04-03

    Medicine, N.L.: Medical Subject Headings Ontology (2025). https://www.nlm.nih.gov/ mesh/meshhome.html Accessed 2025-04-03

    2025

  37. [45]

    Review of Capital as Power2(1), 1–29 (2021)

    Cherizola, J.S.: From commodities to assets: Capital as power and the ontology of finance. Review of Capital as Power2(1), 1–29 (2021). Place: s.l. Publisher: Forum on Capital As Power - Toward a New Cosmology of Capi- talism 18

    2021

  38. [46]

    In: Hor- rocks, I., Hendler, J

    Missikoff, M., Navigli, R., Velardi, P.: The usable ontology: An environment for building and assessing a domain ontology. In: Hor- rocks, I., Hendler, J. (eds.) The Semantic Web — ISWC 2002. Lecture Notes in Com- puter Science, pp. 39–53. Springer, Berlin, Heidelberg (2002)

    2002

  39. [47]

    In: Andersen, K.V., Deben- ham, J., Wagner, R

    De Nicola, A., Missikoff, M., Navigli, R.: A proposal for a unified process for ontology building: UPON. In: Andersen, K.V., Deben- ham, J., Wagner, R. (eds.) Database and Expert Systems Applications. Lecture Notes in Computer Science, pp. 655–664. Springer, Berlin, Heidelberg (2005)

    2005

  40. [48]

    In: 2009 17th IEEE International Requirements Engineering Conference, pp

    Mader, P., Gotel, O., Philippow, I.: Motiva- tion Matters in the Traceability Trenches. In: 2009 17th IEEE International Requirements Engineering Conference, pp. 143–148 (2009). ISSN: 2332-6441

    2009

  41. [49]

    In: 2024 IEEE 32st International Requirements Engineering Conference (RE)

    Abdeen, W., Unterkalmsteiner, M., Krzysztof, W., Chirtoglou, A., Schimanski, C., Goli, H.: Multi-label requirements classi- fication with large taxonomies. In: 2024 IEEE 32st International Requirements Engineering Conference (RE). Requirements Engineering Conference. IEEE, Pis...

    2024

  42. [50]

    Expert Systems 40(1), 13098 (2023)

    Unterkalmsteiner, M., Abdeen, W.: A com- pendium and evaluation of taxonomy qual- ity attributes. Expert Systems 40(1), 13098 (2023)

    2023

  43. [51]

    https://github

    Council), E.D.M.C.E.: Financial Indus- try Business Ontology. https://github. com/edmcouncil/fibo?tab=readme-ov-file. Accessed = 2024-10-13

    2024

  44. [52]

    https://data.nasa.gov/ ontologies/atmonto/data

    NASA: The NASA Air Traffic Management Ontology (atmonto). https://data.nasa.gov/ ontologies/atmonto/data. Accessed = 2024- 10-13

    2024

  45. [53]

    Bioinformatics 40(3), 104 (2024)

    Caufield, J.H., Hegde, H., Emonet, V., Har- ris, N.L., Joachimiak, M.P., Matentzoglu, N., Kim, H., Moxon, S., Reese, J.T., Haen- del, M.A., Robinson, P.N., Mungall, C.J.: Structured prompt interrogation and recur- sive extraction of semantics (SPIRES): a method for populating ...

    2024

  46. [54]

    In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Dis- covery & Data Mining

    Zhang, C., Tao, F., Chen, X., Shen, J., Jiang, M., Sadler, B., Vanni, M., Han, J.: TaxoGen: Unsupervised topic taxonomy construction by adaptive term embedding and clustering. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Dis- covery & Data Minin...

  47. [55]

    In: Proceedings of the ACM Web Conference 2022

    Lee, D., Shen, J., Kang, S., Yoon, S., Han, J., Yu, H.: TaxoCom: Topic taxonomy comple- tion with hierarchical discovery of novel topic clusters. In: Proceedings of the ACM Web Conference 2022. WWW ’22, pp. 2819–2829. Association for Computing Machinery, New York, NY, USA (2022)

    2022

  48. [56]

    arXiv preprint arXiv:1810.04805 (2018) 19

    Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for lan- guage understanding. arXiv preprint arXiv:1810.04805 (2018) 19

    2018 arXiv

  49. [173]

    Springer, Berling, Heidelberg (2013)

    2013

  50. [2709]

    Association for Computing Machinery, New York, NY, USA (2018)

    2018

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.