Algorithm-driven Development: A proactive approach to improving software quality and reducing defects

Abstract

Ensuring software quality while meeting deadlines and adapting to evolving requirements is a persistent challenge in software engineering practice. This paper introduces Algorithm-Driven Development (ADD), a methodology developed from industrial practice to address recurring challenges in translating requirements into reliable, testable, and maintainable software behavior. ADD translates requirements into algorithmic flowcharts from which acceptance tests are systematically derived. These flowcharts serve both as specification artifacts and as technical blueprints, supporting shared understanding between stakeholders and developers. By linking requirement modeling with automated test generation, ADD provides systematic coverage of functional scenarios, including edge cases, from the outset of development. The approach was evaluated over a four-year period within an industrial project at Dassault Syst\`emes, involving two development teams, 22,444 lines of production code for Team 1, and 157 APIs analyzed for Team 2. The evaluation combined longitudinal quality and delivery indicators with a comparative analysis of ADD, TDD, and test-last development practices across API functions of different complexity levels. Empirical data collected from internal lifecycle management and CI/CD systems show that ADD supported sustained code coverage above 95%, low defect density in both QA and post-release phases, and a stable delivery cadence. These findings provide evidence of ADD's potential to strengthen the connection between requirements, testing, and implementation in industrial software development contexts.