The novel method of co-evolution labeling for predictive test optimization is introduced, deriving test relevance from tests and code changing together in the version history, which nearly matches the failure detection capabilities of failure-based models, while being more resistant to label noise and requiring no test result history.
It is argued that AI is unlikely to fully replace human testers in the near future and should be used as an assistant that supports human judgment in software quality assurance.
The analysis indicates that no single model is universally optimal; robust software failure prediction requires dataset-aware preprocessing, leakage-safe validation, imbalance-aware evaluation, and an explicit trade-off among predictive performance, computational efficiency, and interpretability.
Prasad Mathapati, S. G. Gollagi, Zebashireen Fahim Shaikh· International journal of res...· 0 citations
Three different types of search options are proposed to reduce the overall search time of iFixFlakies: split options that leverage hierarchical, code similarity, and historical information to better form the subsequences for further search, a pick option that prioritizes running a subsequence of tests based on its expe...
Suzzana Rafi, Meenam Pious, Shanto Rahman et al.· 0 citations
Reliable assessment of LLM-generated tests should treat executability as a gate and combine coverage with mutation testing and structural quality indicators, and in practice, model selection should precede prompt tuning.
Bilal Al-Ahmad, M. Harshvardhan, Khaled El-Fakih et al.· 0 citations
PROBO is presented, an iterative approach that leverages JVM runtime metrics to guide Bayesian optimization for testing time reduction and generates candidate flag configurations through three complementary strategies guided by expected testing time improvement.
Abdelrahman Baz, Wing Lam, August Shi· 0 citations
This work examines the suitability of regression trees for fast (nano- to microsecond-scale) runtime decisions within operating systems, and is able to reduce inference latency by up to an order of magnitude compared to conventional approaches.
B. Friesel, Marcel Lütke Dreimann, Olaf Spinczyk· Proceedings of the 14th Work...· 0 citations
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