The C-suite is fed up with software disasters putting the quarterly statement at risk as they digitize the business. They will demand more accountability and force improvements in software processes that may clash with agile culture. Business critical applications have become so complex and demand for functionality so immediate that human-based quality practices are no longer sufficient. Developer capabilities must be enhanced by improved software quality technology integrated into DevOps toolchains. Providing deeper intelligence about structural weaknesses and operational risks is enabled by new structural quality measurement standards supplemented by machine learning. Recent results from machine learning research in software quality will be discussed along with some caveats about what to expect. International standards for measuring the structural quality of software developed by the Consortium for IT Software Quality (CISQ) will be reviewed along with results of empirical research on how some of the most severe flaws are distributed in business applications. The talk will conclude with organizational requirements for successfully adopting these advances.
• How will 9-digit defects affect software development in the future?
• What standards are available for measuring the structural quality of source code?
• What has machine learning contributed to software quality and what can we expect?