Our Notes

Posts about research, open source and technologies.

Stakeholder Analysis: Another Key Piece of Product Inception for Building a Successful Software Product

Stakeholders are a vital part of any software project, which makes Stakeholder Analysis a critical part of the Product Inception phase. The only way to make sure that we are building a product that is relevant and valuable is to engage people and understand why they may be interested in the product and bring them into the process. If we don't involve stakeholders from the very beginning, we can easily build an excellent product with great features, but it's possible that no one will want or need to use it.

  • Product Vision Board: the First Step to Discovering a Successful Software Product through Product Inception

    In the first installment of the Product Inception Series, we will explore Product Vision and, more specifically, the Product Vision Board. This is the first step we always take during the Product Inception phase, because the Product Vision should guide every other decision we make about the product. Read on to find our more about defining your product vision and successfully completing the Product Vision Board and start the Product Inception phase off on the right foot.

  • Hello sophilabs, welcome to NYC!

    We are thrilled to announce that sophilabs has expanded to Brooklyn, NY! Our new office is located in a popular co-working space in the DUMBO neighborhood of Brooklyn and is home base to a small group of developers who have joined the sophilabs team.

  • Building Blocks of an Agile Transformation: Develop & Execute a Framework of Small, Continuous Improvements

    In our first blog entry about 'Planning your Journey' from the 'Building Blocks of an Agile Transformation series, we introduced concepts and elaborated explanations key to Agile philosophy: its core values, principles overview, brief history and most importantly, how being Agile fits within a 'Continuous Improvement' mindset.

  • Node.js Pull Request Merge Prediction using Machine Learning

    In this article I will explain a simple use of Machine Learning (ML) to predict if it is possible to accurately predict whether a Pull Request (PR) will be accepted when it is created. The aim of this experiment is to generate a reliable program which can aid a Project integrator in managing PRs for a particular project. Considering nowadays large software projects have a constant flow of new PRs, this work explores a replicable model to help filter out unwanted Pull Requests as soon as possible.

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