Stanislav Moiseev

Grigoriy Bokov, Aleksandr Konovalov, Anna Uporova et al.

Dynamic community detection must update high-quality modularity partitions after edge batches, yet full Leiden reruns make small changes scale with the whole snapshot. Existing dynamic methods reduce work but often alter Leiden refinement, keep limited hierarchy state, or restrict graph support. This paper presents LD-Leiden, a local dynamic Leiden method for weighted directed and undirected graphs that preserves the move-refine-aggregate pipeline and updates only repaired affected regions. Its novelty is the combination of an affected-frontier rule after statistic repair, exact subtract-add aggregate repair, and conflict-filtered parallel local moves; together these mechanisms bound update cost by the visited frontier rather than the full graph. On real streams and streamed static graphs with up to 214M vertices and 3.30B edges, LD-Leiden is 48.77x faster than warm-started Leidenalg in 100-batch runs while preserving a 0.996 final modularity ratio. On the common undirected benchmark set, it is 6.94x faster than DF-Leiden and 9.73x faster than NetworKit while obtaining higher final modularity; synthetic sequences support the predicted local edge-volume scaling.

Artem Chervyakov, Alexander Kharitonov, Pavel Zadorozhny et al.

Advancements in LLMs have enhanced task automation in software engineering; however, current evaluations primarily focus on natural language tasks, overlooking code quality. Most benchmarks prioritize high-level reasoning over executable code and real-world performance, leaving gaps in understanding true capabilities and risks associated with these models in production. To address this issue, we propose MERA Code, a new addition to the MERA benchmark family, specifically focused on evaluating code for the latest code generation LLMs in Russian. This benchmark includes 11 evaluation tasks that span 8 programming languages. Our proposed evaluation methodology features a taxonomy that outlines the practical coding skills necessary for models to complete these tasks. The benchmark comprises an open-source codebase for users to conduct MERA assessments, a scoring system compatible with various programming environments, and a platform featuring a leaderboard and submission system. We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages. We are publicly releasing MERA to guide future research, anticipate groundbreaking features in model development, and standardize evaluation procedures.

Elena Bruches, Daniil Grebenkin, Mikhail Klementev et al.

We present RM-RF, a lightweight reward model for run-free evaluation of automatically generated unit tests. Instead of repeatedly compiling and executing candidate tests, RM-RF predicts - from source and test code alone - three execution-derived signals: (1) whether the augmented test suite compiles and runs successfully, (2) whether the generated test cases increase code coverage, and (3) whether the generated test cases improve the mutation kill rate. To train and evaluate RM-RF we assemble a multilingual dataset (Java, Python, Go) of focal files, test files, and candidate test additions labeled by an execution-based pipeline, and we release an associated dataset and methodology for comparative evaluation. We tested multiple model families and tuning regimes (zero-shot, full fine-tuning, and PEFT via LoRA), achieving an average F1 of 0.69 across the three targets. Compared to conventional compile-and-run instruments, RM-RF provides substantially lower latency and infrastructure cost while delivering competitive predictive fidelity, enabling fast, scalable feedback for large-scale test generation and RL-based code optimization.

Pavel Plyusnin, Aleksey Antonov, Vasilii Ermakov et al.

In modern software development change-based testing plays a crucial role. However, as codebases expand and test suites grow, efficiently managing the testing process becomes increasingly challenging, especially given the high frequency of daily code commits. We propose Targeted Test Selection (T-TS), a machine learning approach for industrial test selection. Our key innovation is a data representation that represent commits as Bags-of-Words of changed files, incorporates cross-file and additional predictive features, and notably avoids the use of coverage maps. Deployed in production, T-TS was comprehensively evaluated against industry standards and recent methods using both internal and public datasets, measuring time efficiency and fault detection. On live industrial data, T-TS selects only 15% of tests, reduces execution time by $5.9\times$, accelerates the pipeline by $5.6\times$, and detects over 95% of test failures. The implementation is publicly available to support further research and practical adoption.