As the digital economy continues to grow and evolve, organizations are quickly realizing that their custom software is their competitive advantage. Some of the consequences of this is that the industry is starting to see an increased focus reducing development cycle times by applying agile development techniques and DevOps process automation.
Automated software analysis must be an integral part of any organization's DevOps strategy; however, many organizations have not yet integrated static analysis on an enterprise basis. Those that have made the shift to enterprise software models and automated analysis are reaping the rewards. The immediate benefit of static analysis is decreased development time and an increased ability to accurately scope and estimate development efforts. Large software code bases are impossible to fully comprehend without tools, particularly with legacy systems where the original architects and developers have retired or moved on. The savings that come from automation are considerable; customers have reported that they reduce their engineering time by up to 80 percent. This includes projects such as COBOL version uplifts, identifying problematic performance bottlenecks and mass change due to new requirements.
Automated analysis also enables organizations to manage the technical debt that occurs when shortcuts are taken over time, which is inevitable in large legacy systems. Due to their complexity, technical debt projects are difficult to undertake manually and are therefore often delayed until a major new requirement or regulation forces the issue. However, once the code is modeled, code quality can be assessed and improved over time using industry standard metrics. Also important is identifying and removing dead and cloned code, which can cost organizations $1 per line of code to maintain each year.
A further benefit of software models is the capability to automatically identify and manage business rules. Business rules are notoriously difficult to identify by hand because the implementation of a rule often crosses program boundaries and interfaces. Program slicing techniques are used to identify logic involved with a particular rule pattern. Once the rules are extracted and combined with business terminology, they can be used in decision models, business rule engines or extended to new software.
All organizations with legacy code should look to add automated analysis to their DevOps cycle. The benefits are substantial and immediate.