# Software Engineering

SWE-1 is our first model family designed to optimize the entire software engineering process, aiming to accelerate software development by 99%. Unlike traditional models that can only write code, SWE-1 not only writes code but also handles terminal operations, accesses other knowledge and the internet, tests products, and understands user feedback. The SWE-1 series includes three models: SWE-1, SWE-1-lite, and SWE-1-mini, catering to different user needs.

SWE-RL is a reinforcement learning-based large language model reasoning technique proposed by Facebook Research, aiming to leverage open-source software evolution data to improve model performance in software engineering tasks. This technology optimizes the model's reasoning capabilities through a rule-driven reward mechanism, enabling it to better understand and generate high-quality code. The main advantages of SWE-RL lie in its innovative reinforcement learning approach and effective utilization of open-source data, opening up new possibilities in the field of software engineering. The technology is currently in the research phase and does not yet have a defined commercial pricing, but it shows significant potential in improving development efficiency and code quality.

SWE-Lancer, launched by OpenAI, is a benchmark designed to assess the performance of cutting-edge language models in real-world freelance software engineering tasks. This benchmark encompasses a range of independent engineering tasks, from $50 bug fixes to $32,000 feature implementations, as well as managerial tasks such as selecting between technical implementation options. By mapping model performance to monetary value, SWE-Lancer offers a new perspective on researching the economic impact of AI model development and promoting the advancement of related research.

Agentless is an agentless method for automatically resolving software development issues. It addresses each problem through three stages: localization, repair, and patch validation. Agentless utilizes a hierarchical process to locate failures down to specific files, related classes or functions, and fine-grained edit locations. It then samples multiple candidate patches based on these locations and selects regression tests to run, generating additional reproduction tests to replicate the original error. The results of these tests are used to re-rank all remaining patches to select a submission. Agentless is currently the top-performing open-source method on SWE-bench lite, with 82 fixes and a resolution rate of 27.3%, averaging a cost of $0.34 per problem.

Lingma SWE-GPT is an open-source large language model focused on tasks within the software engineering domain, aimed at providing intelligent development support. The model is based on the Qwen series foundational models and has undergone additional training to enhance its capabilities in complex software engineering tasks. It ranks highly on authoritative leaderboards for intelligent agents in software engineering, making it suitable for development teams and researchers seeking automation in software enhancement.

Poolside is an advanced foundational AI model built for software engineering challenges. It fine-tunes on user code to learn the unique aspects of projects, allowing it to understand complexities that general models cannot. Built on the foundations of Poolside, it continually improves every day. In addition to its advanced coding model, Poolside also features an intuitive editor assistant and provides an API for developers to build upon. Founded by Jason Warner and Eiso Kant in April 2023, they have extensive experience in AI and software engineering.

SWE-bench Verified is a subset of SWE-bench released by OpenAI that has been manually verified to reliably assess the ability of AI models to solve real-world software issues. It challenges AI to generate patches that resolve the described problems by providing code repositories and problem descriptions. This tool has been developed to improve the accuracy of evaluating the model's ability to autonomously perform software engineering tasks and is a key component of OpenAI's medium-risk framework.

Genie is an AI software engineering model that achieved a 30% score in the SWE-Bench industry standard benchmark test, far surpassing competing products. Genie can independently or collaboratively address bugs, build features, and refactor code, much like collaborating with a colleague. By directly integrating with the GitHub issue tracker, it streamlines workflows and ensures a complete understanding of specific tasks and objectives.

Babel introduces a novel software engineering paradigm encompassing AI-generated components, structured programming, real-time coding, holographic observation, and NoOps. Babel's self-driven agents collaborate to handle coding, debugging, testing, and deployment while simultaneously automating application maintenance and operations.

Cognition Labs is an AI applications laboratory focused on reasoning capabilities. The code they develop is just the beginning. The company aims to enhance software engineering efficiency through AI technology and has launched Devin, an AI software engineer.

Cubed is a platform for AI-generated tasks. By connecting to a GitHub code repository, it uses artificial intelligence to generate specific and actionable task cards. After setting up syncing, Cubed intelligently uses the code repository's context to refine each new task, providing developers with more guidance, accelerating their workflow, and freeing up focus time.

TeamStation is a remote software engineering team expansion SaaS platform powered by Framework Science's unique technology. It allows you to quickly build nearshore software development teams in Latin America. Integrating AI and Fintech, TeamStation offers human-centric AI and Fintech services. It was registered with a US patent in 2022. Users can easily hire top-tier nearshore software engineers with just a few clicks.