One Article Review
| Source |  ProofPoint |
|---|---|
| Identifiant | 8463763 |
| Date de publication | 2024-03-14 06:00:19 (vue: 2024-03-14 13:07:54) |
| Titre | Comment nous avons déployé Github Copilot pour augmenter la productivité des développeurs How We Rolled Out GitHub Copilot to Increase Developer Productivity |
| Texte | Engineering Insights is an ongoing blog series that gives a behind-the-scenes look into the technical challenges, lessons and advances that help our customers protect people and defend data every day. Each post is a firsthand account by one of our engineers about the process that led up to a Proofpoint innovation. Inspired by the rapid rise of generative artificial intelligence (GenAI), we recently kicked off several internal initiatives at Proofpoint that focused on using it within our products. One of our leadership team\'s goals was to find a tool to help increase developer productivity and satisfaction. The timing was perfect to explore options, as the market had become flush with AI-assisted coding tools. Our project was to analyze the available tools on the market in-depth. We wanted to choose an AI assistant that would provide the best productivity results while also conforming to data governance policies. We set an aggressive timeline to analyze the tools, collaborate with key stakeholders from legal, procurement, finance and the business side, and then deploy the tool across our teams. In the end, we selected GitHub Copilot, a code completion tool developed by GitHub and OpenAI, as our AI coding assistant. In this post, we walk through how we arrived at this decision. We also share the qualitative and quantitative results that we\'ve seen since we\'ve introduced it. Our analysis: approach and criteria When you want to buy a race car-or any car for that matter-it is unlikely that you\'ll look at just one car before making a final decision. As engineers, we are wired to conduct analyses that dive deeply into all the possible best options as well as list all the pros and cons of each. And that\'s what we did here, which led us to a final four list that included GitHub Copilot. These are the criteria that we considered: Languages supported IDEs supported Code ownership Stability AI models used Protection for intellectual property (IP) Licensing terms Security Service-level agreements Chat interface Innovation Special powers Pricing Data governance Support for a broad set of code repositories We took each of the four products on our shortlist for a test drive using a specific set of standard use cases. These use cases were solicited from several engineering teams. They covered a wide range of tasks that we anticipated would be exercised with an AI assistant. For example, we needed the tool to assist not just developers, but also document writers and automation engineers. We had multiple conversations and in-depth demos from the vendors. And when possible, we did customer reference checks as well. Execution: a global rollout Once we selected a vendor, we rolled out the tool to all Proofpoint developers across the globe. We use different code repos, programming languages and IDEs-so, we\'re talking about a lot of permutations and combinations. Our initial rollout covered approximately 50% of our team from various business units and roles for about 30 days. We offered training sessions internally to share best practices and address challenges. We also built an internal community of experts to answer questions. Many issues that came up were ironed out during this pilot phase so that when we went live, it was a smooth process. We only had a few issues. All stakeholders were aware of the progress, from our operations/IT team to our procurement and finance teams. Our journey from start to finish was about 100 days. This might seem like a long time, but we wanted to be sure of our choice. After all, it is difficult to hit “rewind” on an important initiative of this magnitude. Monitoring and measuring results We have been using GitHub Copilot for more than 150 days and during that period we\'ve been collecting telemetry data from the tool and correlating it with several productivity and quality metrics. Our results have been impressive. When it comes to quantitative results, we have seen a general increase in |
| Envoyé | Oui |
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| Tags | Tool Cloud Technical |
| Stories | |
| Notes | ★★★ |
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