a-deep-dive-into-measuring-dependency-freshness-with-libyear-by-scott-ford.json

{
    "title": "A Deep Dive into Measuring Dependency Freshness with LibYear",
    "description": "\"A Deep Dive into Measuring Dependency Freshness with LibYear\" by: Scott Ford\n\nLibYear is a dependency freshness measure which helps you learn how out of date your project\u2019s dependencies are. While LibYear has considerable value when used as a \u201cspot\u201d metric, something that you just measure once, there is even more power that can be unlocked when you observe how the metric has trended over time. In this talk, we\u2019ll explore a tool, libmetrics, which is able to compute this metric across a project\u2019s history. The libmetrics tool supports many different dependency management tools from many different frameworks. Also during this talk, we\u2019re going to look at graphs of LibYear over time for many different open source projects. By analyzing these graphs, we can see the long term impacts of different decisions, such as when a team decides to start using Dependabot.\n\nRecorded at the 2020 Python Web Conference (https://2020.pythonwebconf.com)",
    "thumbnail_url": "https://i.ytimg.com/vi/JR9aRD1pmkQ/hqdefault.jpg",
    "duration": 1882,
    "speakers": [
        "Scott Ford"
    ],
    "language": "eng",
    "recorded": "2020-06-18",
    "videos": [
        {
            "type": "youtube",
            "url": "https://www.youtube.com/watch?v=JR9aRD1pmkQ"
        }
    ],
    "related_urls": [
        {
            "label": "Conference schedule",
            "url": "https://2020.pythonwebconf.com/schedule"
        },
        {
            "label": "Talk announcement",
            "url": "https://2020.pythonwebconf.com/presentations/a-deep-dive-into-measuring-dependency-freshness-with-libyear.html"
        }
    ],
    "tags": [
        "PythonWebConf",
        "PythonWebConf2020"
    ]
}