相关论文: Smart Build Targets Batching Service at Google

Force-directed algorithms are widely used to generate aesthetically pleasing layouts of graphs or networks arisen in many scientific disciplines. To visualize large-scale graphs, several parallel algorithms have been discussed in the…

Large enterprises often operate extensive Continuous Integration (CI) pipelines on large, heterogeneous compute clusters, where conservative, statically defined resource requirements are used to ensure build reliability. This practice leads…

While scheduling and dispatching of computational workloads is a well-investigated subject, only recently has Google provided publicly a vast high-resolution measurement dataset of its cloud workloads. We revisit dispatching and scheduling…

While Large Language Model-based agents have demonstrated substantial progress in task completion, existing evaluation benchmarks tend to overemphasize single-task performance, with insufficient attention given to the crucial aspects of…

Code review is a popular practice where developers critique each others' changes. Since automated builds can identify low-level issues (e.g., syntactic errors, regression bugs), it is not uncommon for software organizations to incorporate…

The increasing adoption of large language models (LLMs) necessitates inference serving systems that can deliver both high throughput and low latency. Deploying LLMs with hundreds of billions of parameters on memory-constrained GPUs exposes…

In neural network topologies, algorithms are running on batches of data tensors. The batches of data are typically scheduled onto the computing cores which execute in parallel. For the algorithms running on batches of data, an optimal batch…

Despite the recent advances showing that a model pre-trained on large-scale source code data is able to gain appreciable generalization capability, it still requires a sizeable amount of data on the target task for fine-tuning. And the…

Objective. We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome…

The availability of large-scale datasets, advanced architectures, and powerful computational resources have led to effective code models that automate diverse software engineering activities. The datasets usually consist of billions of…

Continuous fuzzing is an increasingly popular technique for automated quality and security assurance. Google maintains OSS-Fuzz: a continuous fuzzing service for open source software. We conduct the first empirical study of OSS-Fuzz,…

Code generation aims to automatically generate source code from high-level task specifications, which can significantly increase productivity of software engineering. Recently, approaches based on large language models (LLMs) have shown…

Robustness is a key concern for Rust library development because Rust promises no risks of undefined behaviors if developers use safe APIs only. Fuzzing is a practical approach for examining the robustness of programs. However, existing…

Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and transient behavior. Compared to traditional (batch) data…

The focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Yet, companies with a long history of rapid delivery exist. Our primary aim is to…

Code completion, one of the most useful features in the Integrated Development Environments (IDEs), can accelerate software development by suggesting the libraries, APIs, and method names in real-time. Recent studies have shown that…

Current benchmarks for coding evaluate language models (LMs) on concrete, well-specified tasks such as fixing specific bugs or writing targeted tests. However, human programmers do not spend all day incessantly addressing isolated tasks.…

The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic…

Cloud systems are becoming increasingly powerful and complex. It is highly challenging to identify anomalous execution behaviors and pinpoint problems by examining the overwhelming intermediate results/states in complex application…

Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more challenging than grid and cluster environ- ments. This is because a user may have a budgetary constraint or a deadline for executing the BoT application in order to keep…