In modern cloud architectures, multiple applications often run within the same clustered environment, sharing underlying resources. This resource sharing can cause interference among applications, leading to degraded latency and reduced system stability. As containerized microservices become increasingly central to cloud-native applications, their performance can suffer from complex interference scenarios related to resource competition. Meanwhile, most existing microservice approaches address interference either by detecting and localizing performance issues or by optimizing latency alone, without explaining why specific co-locations cause cross-application interference, and how this can inform service placement optimization. This work closes that gap by building a spatio-temporal data structure that captures the causal effects of cross-application interference. These causal effects are mathematically formalized as necessary and sufficient conditional probabilities that inform a multi-objective optimizer (Optuna). Cross-application profiling is used to simulate traces and estimate interference probabilities, while per-service latency baselines are provided by performance data, such as 95th-percentile response times (p95). Our approach supports network penalties, application isolation requirements, and adjustable weighting of necessary and sufficient causal metrics. Experimental results on real multi-application workloads show that interference-aware placements significantly reduce cross-application interference and improve response performance. Ultimately, the causality-driven multi-objective formulation gives cloud operators explicit control over interference, latency, and communication overhead when configuring service placements.

Background] Efficient code review is essential in industrial software development, but writing high-quality Pull Request (PR) descriptions aligned with core software engineering competencies remains challenging. [Aims] To investigate Large Language Models (LLMs) as a reliable PR description assistant, we examined two research problems: (1) how developer competencies relate to real-world PR quality, and (2) how developers perceive PR quality and their variants (LLM-Improved). [Methods] We adopted six software engineering competencies to prompt six LLMs to score 212,687 PRs from 82 opensource projects spanning three repository archetypes. From these, we selected the top six PRs for a controlled experiment with 38 software professionals, using a$6 \times 6$Latin square design to measure preferences. The LLMs generated three variants of each original (O) PR: degraded (D), improved from the original (IO), and improved from the degraded (ID). We then compared these variants across LLMs for semantic, lexical, and stylistic similarity. [Results] Surprisingly, our controlled experiment with 38 software professionals showed that the ID variant was rated significantly higher than both the Original PR (O) and the LLMDegraded (D) one. However, the IO variant was not significantly better, and participants complained about their verbosity and the “AI tone”. Concerning generalizability, the six LLMs produced semantically similar PRs, but with high lexical and stylistic variation. [Conclusions] This suggests LLMs can enhance PR by providing missing structure but require guidance to retain human nuance. Overall, our findings support human-centered, modelaware integration of LLMs to strengthen competency-aligned code review.

Microtask crowdsourcing organizes complex work into workflows, decomposing large tasks into small, relatively independent microtasks. Applied to software development, this model might increase participation in open source software development by lowering the barriers to contribu-tion and dramatically decrease time to market by increasing the parallelism in development work. To explore this idea, we have developed an approach to decomposing programming work into microtasks. Work is coordinated through tracking changes to a graph of artifacts, generating appropriate microtasks and propagating change notifications to artifacts with dependencies. We have implemented our approach in CrowdCode, a cloud IDE for crowd development. To evaluate the feasibility of microtask programming, we performed a small study and found that a small crowd of 12 workers was able to successfully write 480 lines of code and 61 unit tests in 14.25 person-hours of time.