Justin (Schmitz et al.) brings hybrid CPU+memory auto-scaling to Apache Flink, extending the Flink Kubernetes Operator to fine-tune parallelism and memory per operator instead of one-size-fits-all scaling. doi.org/10.1016/j.scico.2026…

Zhang and Wang propose a Hidden Markov Random Field-based fault detection and RBF NN-based fault tendency prediction method for SaaS application modules. It achieves high precision/recall for fault judgment and prediction, with low energy consumption. doi.org/10.1016/j.scico.2026…

uvl2dimacs (Heradio et al.) converts Universal Variability Language models into DIMACS format using Tseitin transformation and backbone simplification. It cuts clauses in 99% of cases (median 40%) on 1533 real-world models. doi.org/10.1016/j.scico.2026…

Li et al. evaluate 24 Graph-GNN combinations (6 program graphs x 4 GNN model types) for software defect prediction across 7 Java projects. CFG-GCN performs best for within-project prediction, while CPG-GAT excels for cross-project prediction. doi.org/10.1016/j.scico.2026…

Van Cuyck et al. present 3 approaches for checking mutual acceptance in specification models. These are an algorithmic specification check, a two-procedure conformance test, and an optimized combined version that trades genericity for efficiency. doi.org/10.1016/j.scico.2026…

RebeCaos (Proença and ter Beek) is a web-based front-end tool for Rebeca that allows simulating different (timed) Rebeca semantics. It offers initial reachability analyses for Rebeca models, e.g., to detect deadlocks or check desirable states. doi.org/10.1016/j.scico.2026…

ArithHomFA (Waga et al.) uses fully homomorphic encryption for oblivious online monitoring of discrete-time STL, allowing a remote monitor to check signal properties without decrypting the data. It has been used to monitor vehicle behavior. doi.org/10.1016/j.scico.2026…

C2VR (Xia et al.) migrates C code with ACSL specs into verifiable Rust with Verus specs, verifying 139/175 programs (92 automatically). Failures are classified into 15 symptoms, 10 root causes, and 9 repair strategies. doi.org/10.1016/j.scico.2026…

Martínez-Castañón et al. propose a DSL to optimize periodic vehicle fleet maintenance scheduling using Model-Driven Engineering. Usability tests show it reduces time and effort compared to CMMS tools and increases user satisfaction. doi.org/10.1016/j.scico.2026…

Kashef and Ghassemi model social network user behavior by clustering users and learning automata (L*, KV, L*P) with SVM classification. The deterministic models achieved 65–75% accuracy, improving up to 12% with probabilistic strategies. doi.org/10.1016/j.scico.2026…

Rhazi et al. apply contrastive learning with inter-sample attention for defect prediction, capturing relationships between software classes beyond raw metrics. The method outperformed 5 baselines on 9 projects across 3 benchmarks. doi.org/10.1016/j.scico.2026…

Benjamin and Mathew triangulate Stack Overflow discourse, GitHub adoption data, and GitHub Actions telemetry to predict CI/CD reliability risks. Deep representation learning outperforms linear baselines at early-warning recall for repository instability. doi.org/10.1016/j.scico.2026…

AugmenTest (Khandaker et al.) uses LLMs to generate JUnit assertions from natural language docs/comments alone. It was tested on a curated benchmark of real-world Java test cases. sciencedirect.com/science/ar…

Carvalho et al. define a natural-language semantics for UML state machines/composite structures and integrate it with NAT2TEST for combined validation and verification. This was demonstrated on the dining philosophers and a distributed ring-buffer model. doi.org/10.1016/j.scico.2026…

Pizarro et al. conducted a 20-month industry study on LLM test generation, finding that high-performing models exhibited unexpected regressions. The authors propose a GQM-based continuous evaluation framework (Tetrics) for informed LLM adoption. doi.org/10.1016/j.scico.2026…

Benjamin and Mathew triangulate Stack Overflow discourse, GitHub adoption data, and 513K+ workflow logs to model CI/CD reliability. A DL model trained on these combined signals outperforms linear baselines in predicting repos at risk of instability. sciencedirect.com/science/ar…

SMTQuery (Kulczynski et al.) is a framework for maintaining and querying SMT-LIB string benchmarks, enabling deep analysis of solver problems. Applied to 100K+ benchmarks, it trained an algorithm-selection model to match benchmarks to suitable solvers. doi.org/10.1016/j.scico.2026…

Muroya et al. introduce Term Evaluation (and Refinement) Systems, a rewriting-theoretic framework that uses local coherence and critical pair analysis to prove the contextual improvement of program refinements in 1st- and typed 2nd-order settings. sciencedirect.com/science/ar…

ExDA (Ding et al.) is an augmentation strategy for Cross-Project Defect Prediction using feature importance + selective VAE to augment data. In small CPDP, it improves recall and interpretability in small-sample transfer settings. sciencedirect.com/science/ar…

Chen et al. analyze how function-level vs parameter-related and semantic/structural code context affects parameter description generation across DL models. Full AST is best; combining semantics and syntax rarely helps; many parameters hurt performance. sciencedirect.com/science/ar…