The Algorithmic Autoregulation Software Development Methodology
Renato Fabbri, Ricardo Fabbri, Vilson Vieira, Daniel Penalva, and Danilo Shiga, Marcos Mendonca, Alexandre Negrao, Lucas, Zambianchi, Gabriela Thume

TL;DR
The paper introduces Algorithmic Autoregulation, a self-regulating methodology for distributed software teams that enhances efficiency and coordination through voluntary logging, peer validation, and minimal management, supported by real-world case studies.
Contribution
It presents a novel self-regulating approach for distributed team coordination based on social networking concepts and peer validation, with demonstrated real-world effectiveness.
Findings
Increased team efficiency observed in real-world GSD scenarios.
Reduced need for central management and meetings.
Enhanced documentation and asynchronous communication.
Abstract
We present a new self-regulating methodology for coordinating distributed team work called Algorithmic Autoregulation (AA), based on recent social networking concepts and individual merit. Team members take on an egalitarian role, and stay voluntarily logged into so-called AA sessions for part of their time (e.g. 2 hours per day), during which they create periodical logs - short text sentences - they wish to share about their activity with the team. These logs are publicly aggregated in a website and are peer-validated after the end of a session, as in code review. A short screencast is ideally recorded at the end of each session to make AA logs more understandable. This methodology has shown to be well-suited for increasing the efficiency of distributed teams working on Global Software Development (GSD), as observed in our reported experience in actual real-world situations. This…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Open Source Software Innovations · Software Engineering Research
