Fail Fast - Fail Often: Enhancing Agile Methodology using Dynamic Regression, Code Bisector and Code Quality in Continuous Integration (CI)

TL;DR

This paper proposes an enhanced Agile methodology integrating dynamic regression, code bisector, and code quality tools within continuous integration to address testing and deployment challenges.

Contribution

It introduces a novel approach combining specific tools and techniques to improve Agile practices in continuous integration environments.

Findings

Improved detection of code issues through dynamic regression.

Enhanced code quality with integrated tools.

Streamlined Agile workflows in CI environments.

Abstract

Agile practices are receiving considerable attention from industry as an alternative to traditional software development approaches. However, there are a number of challenges in combining Agile [2] with Test-driven development (TDD) [10] practices, cloud deployments, continuous integration (CI), non-stop performance, load, security and accessibly testing. From these challenges; Continuous Integration is a relatively an approach widely discussed and practiced in software testing. This paper describes an approach for improved Agile Methodology using Code Quality, Code Bisector and Dynamic Regression in Continuous Integration. The set of tools used for this analysis, design and development are Jenkins, Robot Framework [4], Perforce and Git.