Szczegóły publikacji
Opis bibliograficzny
Regression test optimization for software of the cellular network base stations: a language-based approach / Sebastian ZARĘBSKI, Krzysztof RUSEK, Piotr CHOŁDA // Expert Systems with Applications ; ISSN 0957-4174 . — 2026 — vol. 311 art. no. 131225, s. 1–15. — Bibliogr. s. 14–15, Abstr. — Publikacja dostępna online od: 2026-01-27. — S. Zarębski - dod. afiliacja: NOKIA, Kraków
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165933 |
|---|---|
| Data dodania do BaDAP | 2026-04-13 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.eswa.2026.131225 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Expert Systems with Applications |
Abstract
This paper introduces Linear Model of Latent Dirichlet Allocation (LMLDA), a novel methodology for software test optimization that directly addresses the gap between computationally-prohibitive large language models (LLMs) and semantically-shallow heuristics. Our primary contribution is a lightweight, interpretable, and cost-efficient model specifically designed for high-stakes industrial Continuous Integration and Continuous Development (CI/CD) environments where security and traceability are essential. The novelty of LMLDA lies in its integration of Latent Dirichlet Allocation (LDA) for the semantic analysis of code modifications and test content, with a classifier based on logistic regression concepts for the training phase, yet offering prediction capabilities that align with the computational simplicity of linear regression. This approach uniquely predicts the probability of test failure based on semantic interactions, enabling precise, bug-centric prioritization rather than relying on indirect diversity proxies. A large-scale industrial case study at NOKIA demonstrates LMLDA’s practical effectiveness, achieving an average 64% reduction in test suite size while maintaining 88% precision in bug detection and accelerating critical bug discovery by an average of 8 h per cycle.
