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Princeton's SWE-Agent: Revolutionizing Software Engineering 05 Min. Read 12 July 2024 Are we close to having a fully automated software engineer? WhatsApp LinkedIn X (Twitter) Copy link Introduction In the fast-paced world of software development, engineering leaders constantly seek innovative solutions to enhance productivity, reduce time-to-market, and ensure high-quality code. Language model (LM) agents in software engineering workflows promises the possibility to revolutionise how teams approach coding, testing, and maintenance tasks. However, the potential of these agents is often limited by their ability to effectively interact with complex development environments To address this challenge researchers at Princeton published a paper discussing the possibility of a super smart SWE-agent, an advanced system that can maximise the output of LM agents in software engineering tasks using an agent computer interface or ACI, that can navigate code repositories, perform precise code edits, and execute rigorous testing protocols. We will discuss key motivations and findings from this research that can help engineering leaders prepare for the future that GenAI might is promising to create for all of us which we should not afford to ignore What is the need for this? Traditional methods of coding, testing, and maintenance are time-consuming and prone to human error. LM agents have the capability to automate these tasks, but their effectiveness is limited by the challenges they face in interacting with development environments. If LM agents can be made to be more effective at executing software engineering work, it can help engineering managers reduce the workload on human developers, accelerating development cycles, and improving overall software reliability What was their Approach? SWE-agent: a system that facilitates LM agents to autonomously use computers to solve software engineering tasks. SWE-agent’s custom agent-computer interface (ACI) significantly enhances an agent’s ability to create and edit code files, navigate entire repositories, and execute tests and other programs. SWE-agent is an LM interacting with a computer through an agent-computer interface (ACI), which includes the commands the agent uses and the format of the feedback from the computer. LM agents have been so far only used for code generation with moderation and feedback. Applying agents to more complex code tasks like software engineering remained unexplored LM agents are typically designed to use existing applications, such as the Linux shell or Python interpreter. However, to perform more complex programming tasks such as software engineering, human engineers benefit from sophisticated applications like VSCode with powerful tools and extensions. Inspired by human-computer interaction. LM agents represent a new category of end user, with their own needs and abilities. Specialised applications like IDEs (e.g., VSCode, PyCharm) make scientists and software engineers more efficient and effective at computer tasks. Similarly, ACI design aims to create a suitable interface that makes LM agents more effective at digital work such as software engineering The researchers assumed a fixed LM and focused on designing the ACI to improve its performance. This meant shaping their actions, their documentation, and environment feedback to complement an LM’s limitations and abilities Experimental Set-up DataSets : We primarily evaluate on the SWE-bench dataset, which includes 2,294 task instances from 12 different repositories of popular Python packages. We report our main agent results on the full SWE-bench test set and ablations and analysis on the SWE-bench Lite test set. SWE-bench Lite is a canonical subset of 300 instances from SWE-bench that focus on evaluating self-contained functional bug fixes. We also test SWE-agent’s basic code editing abilities with HumanEvalFix, a short-form code debugging benchmark. Models : All results, ablations, and analyses are based on two leading LMs, GPT-4 Turbo (gpt-4-1106-preview) and Claude 3 Opus (claude-3-opus-20240229). We experimented with a number of additional closed and open source models, including Llama 3 and DeepSeek Coder, but found their performance in the agent setting to be subpar. GPT-4 Turbo and Claude 3 Opus have 128k and 200k token context windows, respectively, which provides sufficient room for the LM to interact for several turns after being fed the system prompt, issue description, and optionally, a demonstration. Baselines: We compare SWE-agent to two baselines. The first setting is the non-interactive, retrieval augmented generation (RAG) baselines. Here, a retrieval system retrieves the most relevant codebase files using the issue as the query; given these files, the model is asked to directly generate a patch file that resolves the issue. The second setting, called Shell-only, is adapted from the interactive coding framework introduced in Yang et al. Following the InterCode environment, this baseline system asks the LM to resolve the issue by interacting with a shell process on Linux. Like SWE-agent, model prediction is generated automatically based on the final state of the codebase after interaction. Metrics. We report % Resolved or pass@1 as the main metric, which is the proportion of instances for which all tests pass successfully after the model generated patch is applied to the repository Results The result demonstrated that LM agent called SWE-agent that worked with custom agent-computer-interface or ACI was able to resolve 7 times more software tasks that pass the test bench compare to a RAG using the same underlying models i.e. GPT-4 Turbo and Claude 3 Opus and 64% better performance to Shell-only. This research ably demonstrates the direction that agentic architecture is making (with the right supporting tools) in making a fully functional software engineer a distant but possible eventuality Read the complete paper here and let us know if you believe if this is a step in the positive direction Would you like an autonomous software engineer in your team? Yes No Prevent Logical bugs in your databases calls, queues and external APIs or services Take a Live Tour Book a Demo

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Learn how to overcome TDD challenges with practical tips to improve code quality, boost development speed, and streamline adoption. Best Practices 42 min. Implementing TDD: Organizational Struggles & Fixes Learn how to overcome TDD challenges with practical tips to improve code quality, boost development speed, and streamline adoption. Get Access Speakers Shailendra Singh Founder HyperTest Oliver Zihler Technical Agile Consultant CodeArtify Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

Learn the simple yet powerful way to achieve 90%+ code coverage effortlessly, ensuring smooth and confident releases Best Practices 30 min. Get to 90%+ coverage in less than a day without writing tests Learn the simple yet powerful way to achieve 90%+ code coverage effortlessly, ensuring smooth and confident releases Get Access Speakers Shailendra Singh Founder HyperTest Ushnanshu Pant Senior Solution Engineer HyperTest Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

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This Guide delves right into the transition journey of MAANG from monoliths to microservices, providing the underlying approaches they used to successfully run more than 1000 microservices as of today. Scaling with Microservices MAANG'S Experience This Guide delves right into the transition journey of MAANG from monoliths to microservices, providing the underlying approaches they used to successfully run more than 1000 microservices as of today. Download now Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

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No more Writing Mocks: The Future of Unit & Integration Testing 05 Min Read 7 October 2024 No more Writing Mocks: The Future of Unit & Integration Testing Vaishali Rastogi WhatsApp LinkedIn X (Twitter) Copy link Mocks can be a pain to write and maintain, and they can make your tests brittle. In this blog post, we'll explore why you should ditch the mocks and embrace a new approach to unit and integration testing. Access the Complete Webinar here👇: Why Integration Tests Matter? While unit tests are great for testing individual components in isolation, they don't tell you how your code will behave in a real-world environment where it interacts with ➡️databases, downstream services, message queues, and APIs. Integration tests are essential for uncovering issues that arise from these interactions. The Problem with Mocks To perform integration testing, developers often resort to mocking external dependencies. However, mocks come with their own set of drawbacks: Effort Intensive: Writing and maintaining mocks requires a significant time investment, especially as your project grows. Brittle Tests: Mocks simulate behavior based on assumptions about external systems. As those systems evolve, your mocks can become outdated, leading to false positives in your tests. This means your tests might pass even when your code breaks in production due to changes in external dependencies. Limited Scope: Mocks can quickly become unmanageable when dealing with multiple dependencies like databases, caches, and message queues, often leading developers to abandon integration testing altogether. Every language has a framework, every language has a mocking library. A New Approach: HyperTest for Effortless Integration Testing HyperTest offers a solution to these challenges by automating the creation and maintenance of integration tests. HyperTest = Auto-mocking Here's how it works: Record Mode: HyperTest acts like an APM tool, sitting alongside your application and capturing traffic, including requests, its responses, and interactions with external systems and its response. This creates a baseline of your application's behavior. Replay Mode: When you make changes to your code, HyperTest reruns the captured requests, comparing the new responses and interactions against the established baseline. Any discrepancies, such as changes in database queries or responses, are flagged as potential regressions. Automatic Mock Updates: HyperTest automatically updates its mocks as your external dependencies change, ensuring your tests always reflect the latest behavior and eliminating the risk of stale mocks. Link to watch the complete webinar👇: Benefits of using HyperTest: No More Manual Mocks: HyperTest eliminates the need for hand-written mocks, saving you time and effort. Always Up to Date: By automatically updating mocks, HyperTest ensures your integration tests remain reliable and relevant even as your dependencies evolve. Comprehensive Regression Detection: HyperTest identifies regressions not only in your application's responses but also in its interactions with external systems, providing deeper insights into the impact of your code changes. By automating integration testing and mock management, HyperTest frees you to focus on what matters most: building high-quality software. Prevent Logical bugs in your databases calls, queues and external APIs or services Take a Live Tour Book a Demo

Jabbar from Zoop shares how HyperTest cut post-merge bugs by 80% and improved interservice testing. 05 Min Read 21 February 2025 Zoop.one’s Success Story with HyperTest | Featuring Jabbar Vaishali Rastogi Ushnanshu Pant WhatsApp LinkedIn X (Twitter) Copy link Hi everyone, this is Ushnanshu Pant , Customer Solution Expert at HyperTest. I recently had the pleasure of speaking with Jabbar , who works as an SDE-3 at Zoop.one —a B2B product company specializing in KYC solutions and customer onboarding . We talked about the key testing challenges they faced, how HyperTest transformed their approach, and the tangible impact it has had on their development process. Let’s dive in! 1️⃣ What were the primary challenges you faced in testing before implementing HyperTest? Jabbar: I would like to note down the one is like main challenges like mocking the 3rd party API calls, the database query and the Kafka queue messages. And the second challenge was interservice dependency, like if one service is dependent on the other, we need the mockup or the services whether the services are working fine or not. This was lacking and we got this solved after we implemented HyperTest. 👉 It sounds like dependency management and real-time validation were major roadblocks. 2️⃣ How did HyperTest help address these challenges? Jabbar: Initially, we were just mocking third-party libraries. One potential challenge was detecting dependencies between services—for example, if Service A had some code changes that would affect Service B. Before HyperTest, we were setting up mocks for Service A's responses based on Service B, but we weren’t able to identify whether it would actually pass or fail in production. This interdependency issue, along with memory leaks, was a major concern that HyperTest helped resolve effectively. 👉 So, HyperTest not only streamlined the mocking process but also improved visibility into real production behavior. 3️⃣ Can you share some specific features of HyperTest that you found most beneficial? Jabbar: Certainly, one of the standout features is being able to derive test results directly from actual traffic, which means we can simulate real customer interactions without setups. This was a huge advantage because it also allowed us to predict resource needs like CPU or memory scaling for production. Another important feature was automating third-party API interactions, which eliminated the need to write extra code for these operations. 👉 That’s great! Being able to simulate production traffic and automate dependencies must have streamlined your workflow. 4️⃣ What improvements have you noticed in your development and QA processes after integrating HyperTest? Jabbar: With HyperTest, we've seen a dramatic reduction in bugs in our production environment. Before its integration, we identified 40 to 50% issues post-merge to production. Now, it’s less than 10%. This efficiency not only saves time but also significantly reduces the error rate, which I believe is currently around 7 to 8%. The QA team doesn’t have to wait around anymore; they can instantly check the reports, verify API performance, and highlight any necessary changes. This streamlined process has eased the workload considerably for our team. 👉 That’s a huge drop in post-merge issues! 5️⃣ How has HyperTest transformed the dynamics between developers and the QA team? Jabbar: HyperTest acts like a 'ship rocket' between developers and QA, boosting both efficiency and morale. It minimizes conflicts by clearly delineating responsibilities, which in turn reduces friction and misunderstandings. 👉 That’s a great analogy! When teams spend less time debating bugs and more time building, it’s a win for everyone. 6️⃣ What about the coverage reports provided by HyperTest? How effective are they? Jabbar: The coverage reports from HyperTest are thorough, providing insights into line, branch, and state coverage, among others. These reports help our developers ensure no critical areas are missed, covering edges that might typically be overlooked. It was fantastic catching up with Jabbar and hearing how HyperTest has streamlined testing, improved collaboration, and significantly reduced post-production issues at Zoop.one . Their experience really highlights how the right tools can make all the difference in modern software development. A big thank you to Jabbar for sharing these insights! If you're facing similar testing challenges, feel free to reach out —we’d love to help. 🚀 Prevent Logical bugs in your databases calls, queues and external APIs or services Take a Live Tour Book a Demo