ATime-Consistent Benchmark for Repository-Level Software Engineering Evaluation

Evaluation of repository-aware software engineering systems is often confounded by synthetic task design, prompt leakage, and temporal contamination between repository knowledge and future code changes. We present a time-consistent benchmark methodology that snapshots a repository at time T0, constructs repository-derived code knowledge using only artifacts available before T0, and evaluates on engineering tasks derived from pull requests merged in the future interval (T0, T1]. Each historical pull request is transformed into a natural-language task through an LLM-assisted prompt-generation pipeline, and the benchmark is formalized as a matched A/B comparison in which the same software engineering agent is evaluated with and without repository-derived code knowledge while all other variables are held constant. We also report a baseline characterization study on two open-source repositories, DragonFly and React, using three Claude-family models and four prompt granularities. Across both repositories, file-level F1 increases monotonically from minimal to guided prompts, reaching 0.8081 on DragonFly and 0.8078 on React for the strongest tested model. These results show that prompt construction is a first-order benchmark variable. More broadly, the benchmark highlights that temporal consistency and prompt control are core validity requirements for repository-aware software engineering evaluation.

A spherical harmonic-domain spatial audio signal enhancement method based on minimum variance distortionless response

Spatial audio signal enhancement aims to reduce interfering source contributions while preserving the desired sound field with its spatial cues intact. Existing methods generally rely on impractical assumptions (e.g. no reverberation or accurate estimations of impractical information) or have limited applicability. This paper presents a spherical harmonic (SH)-domain minimum variance distortionless response (MVDR)-based spatial signal enhancer using Relative Harmonic Coefficients (ReHCs) to extract clean SH coefficients from noisy ones in reverberant environments. A simulation study shows the proposed method achieves lower estimation error, higher speech-distortion-ratio (SDR), and comparable noise reduction (NR) within the sweet area in a reverberant environment, compared to a beamforming-and-projection method as the baseline.