Modern cloud services have complex architectures, often comprising many software components, and depend on hundreds of configurations parameters to function correctly, securely, and with high performance. Due to the prevalence of open-source software, developers can easily deploy services using third-party software without mastering the configurations of that software. As a result, configuration errors (i.e., misconfigurations) are among the leading causes of service disruptions and outages. While existing cloud automation tools ease the process of service deployment and management, support for detecting misconfigurations in the cloud has not been addressed thoroughly, likely due to the lack of frameworks suitable for consistent parsing of unstandardized configuration files. This paper introduces ConfEx, a framework that enables discovery and extraction of text-based software configurations in the cloud. ConfEx uses a novel vocabulary-based technique to identify configuration files in cloud system instances with unlabeled content. To extract the information in these files, ConfEx leverages existing configuration parsers and post-processes the extracted data for analysis. We show that ConfEx achieves over 99% precision and 100% recall in identifying configuration files on 7805 popular Docker Hub images. Using two applied examples, we demonstrate that ConfEx also enables detecting misconfigurations in the cloud via existing tools that are designed for configurations represented as key-value pairs, revealing 184 errors in public Docker Hub images.
Developing a software service requires a strict software development life cycle and process. This process demands controlling all application code through source control management as well as a rigorous versioning and branching strategy. However, the platform and infrastructure also benefit from this rigor. Software services must be deployed to a target run time environment and provisioning that environment through manual user actions is tedious and error-prone. Provisioning manually also becomes prohibitive as the number of resources grow and spread globally over multiple regions. The answer …
Mobile devices such as smartphones and tablets have become ubiquitous in today's modern computing landscape. The applications that run on these mobile devices (often referred to as "apps") have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-pr…
Micro-services are a common architectural approach to software development today. An indispensable tool for evolving micro-service systems is A/B testing. In A/B testing, two variants, A and B, are applied in an experimental setting. By measuring the outcome of an evaluation criterion, developers can make evidence-based decisions to guide the evolution of their software. Recent studies highlight the need for enhancing the automation when such experiments are conducted in iterations. To that end, we contribute a novel artifact that aims at enhancing the automation of an experimentation pipeline…
TODO comments are very widely used by software developers to describe their pending tasks during software development. However, after performing the task developers sometimes neglect or simply forget to remove the TODO comment, resulting in obsolete TODO comments. These obsolete TODO comments can confuse development teams and may cause the introduction of bugs in the future, decreasing the software's quality and maintainability. In this work, we propose a novel model, named TDCleaner (TODO comment Cleaner), to identify obsolete TODO comments in software projects. TDCleaner can assist developer…
Automating Static Code Analysis Through CI/CD Pipeline Integration
Gianforte School of Computing Montana State University（蒙大拿州立大学吉安福特计算学院）；Pacific Northwest National Laboratory Idaho National Laboratory（太平洋西北国家实验室爱达荷国家实验室）
Moscow Institute of Physics and Technology（莫斯科物理技术学院）
In this paper we explore the challenges of automating experiments in data science. We propose an extensible experiment model as a foundation for integration of different open source tools for running research experiments. We implement our approach in a prototype open source MLDev software package and evaluate it in a series of experiments yielding promising results. Comparison with other state-of-the-art tools signifies novelty of our approach.
Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for virtualized environment setup and meeting the software dependencies for each service. Despite the emergence of many DevOps cloud automation and orchestration tools, users must still rely on specifying low-level scripting details for service deployment and management using Infrastructure-as-Code (IAC). Using these tools required domain expertise along with a stee…
Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day
DP Technology Beijing China（DP技术北京中国）；AI for Science Institute Beijing China（AI for Science研究院北京中国）；Shanghai Jiao Tong University Shanghai China（上海交通大学上海中国）
AutoEnvScaling: Automating the Data Flywheel with Terminal Agents
Northeastern University（东北大学）；Microsoft（微软）；Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Bridging Natural Language and Formal Specification--Automated Translation of Software Requirements to LTL via Hierarchical Semantics Decomposition Using LLMs
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Guangzhou Institute of Technology of Xidian University（西安电子科技大学广州研究院）；China Academy of Space Technology（中国空间技术研究院）
