Overcoming Barriers to Digital Transformation with AI for Code
When enterprises try to modify or upgrade this code, they face a few barriers—one of which is that every small change in the app’s code could have far-reaching, unexpected consequences. With this in mind, everyone’s a little bit afraid of disturbing the legacy code.
This issue has been gradually becoming more pressing (for decades, in some cases), but it’s a shame to let legacy systems continue to hold back growth now that tools finally exist to start modernising outdated applications. With recent advances in AI, the gridlock caused by large-scale legacy code can finally be broken.
In many large organisations, software development teams are up-to-date with the modern code they use on a regular basis, but they no longer have an accurate record of the source code for many of the applications the company relies on. This is how legacy code forms. Legacy code doesn’t have documentation and is no longer actively developed, but is often updated. It might exist in any language, but was probably written in COBOL or Java, and nobody is really sure how it works anymore. The general consensus is to leave it alone.
Since these applications don’t have tests, it can be extremely difficult for developers to see the connections between lines of code and anticipate the impact of the changes they make or new code they add before bugs or other issues are introduced. It can be even harder to trace the source of the bug and resolve it. As a result, applications built from legacy systems start to feel inaccessible and locked away by a fear of breaking something critical.
Legacy systems can create huge financial burdens for businesses, costing companies millions of dollars each year to work around, without accounting for the opportunity cost of not being able to adopt newer, more efficient tools. In some cases, legacy code is written in languages that most developers don’t know anymore, and editing it means organisations have to entice developers out of retirement to resolve critical issues. It’s not unusual for 80% of an annual IT budget to be allocated to maintaining core legacy applications.
Currently, these challenges are dealt with manually by slogging through the code, refactoring when possible, writing tests as needed and fixing bugs as they appear. This is slow work; in fact, the majority of developers spend less than half of each workday engaging in active development.
