Instabug today revealed it has added an ability to both analyze mobile application crash report data and source code, to better pinpoint the root cause of issues accurately, which it then feeds into a proprietary generative artificial intelligence (AI) platform, dubbed SmartResolve, that automatically generates the code needed to resolve it.
Kenny Johnston, chief product officer for Instabug, said version 2.0 of SmartResolve, available via a private beta, enables application developers to review and apply a suggested fix that is based on actual crash data and source code that its proprietary large language model has been exposed to using retrieval-augmented generation (RAG) techniques.
Replicating errors that have occurred in a mobile application is often exceedingly difficult, noted Johnston. SmartResolve uses traces collected from crash data to automate that process, which is then used to create a fix that can be applied with a single click.
Additionally, SmartResolve can be integrated with code repositories to generate a pull request.
Organizations in the age of digital transformation are, of course, more dependent on mobile applications. In fact, brand identity is now closely associated with how well a mobile application performs. The challenge is that end users are much less tolerant of bugs and performance issues, largely because it’s relatively trivial to replace one mobile application with another. Having access to the latest crash data is critical for organizations that can then leverage AI to resolve issues much faster than they could have previously, said Johnston.
There may even come a day when end users will simply view organizations that deploy buggy applications as simply being too antiquated to rely on, he noted.
It’s not clear to what degree organizations are relying on AI tools and platforms to now build applications. A Techstrong Research survey found a third (33%) of respondents are working for organizations that make use of artificial intelligence (AI) to build software, while another 42% are considering it. Only 6% said they have no plans to use AI. However, only 9% have fully integrated AI into their DevOps pipelines. Another 22% have partially achieved that goal, while 14% are doing so only for new projects. A total of 28% said they expect to integrate AI into their workflows in the next 12 months.
The one clear thing is that application developers are going to naturally drift toward organizations that provide them with access to the best tools. If an AI platform makes it possible to fix issues that would otherwise take time away from writing additional code, many application developers are going to opt to work for organizations that provide that capability versus continuing to require them to manually debug an application.
One way or another, AI is about to transform how applications are built, deployed and maintained. The only issue that remains to be resolved now is to what degree and how soon that transition will take in an era where AI-enabled tools for building applications are pervasively available.
