Instabug announced the release of SmartResolve 2.0, representing an AI-powered breakthrough that promises to transform issue resolution for mobile teams.
SmartResolve 2.0 enables mobile engineering teams to focus on innovation and deliver superior mobile experiences by dramatically reducing the time needed to debug and fix app crashes and stability issues.
SmartResolve 2.0 leverages a proprietary, fine-tuned AI model to analyze crash report data and app source code to pinpoint the root cause of issues accurately. It then automatically generates the code to resolve the issue, leaving developers to simply review and apply the fix with a single click. This reduces manual debugging time by an order of magnitude and enhances the efficiency of mobile app deployment.
"We are committed to helping mobile teams transform app performance while enabling their drive to deliver innovations to their customers," said Omar Gabr, CEO of Instabug. "With SmartResolve 2.0, we're taking a major step toward a future of zero-maintenance mobile apps and delivering on our promise of AI-enabled mobile observability. By drastically reducing the need for manual review of crash reports, developers can focus on what matters most—innovation—while our proprietary AI model ensures peak app performance. This breakthrough not only accelerates mobile-led growth for the enterprise but also sets a new standard for the future of mobile apps."
SmartResolve 2.0 includes several notable features:
- AI-driven crash analysis: Automatically analyzes crash stack traces and app source code to identify root causes.
- Code generation: Automatically generates the necessary code to fix issues, leaving developers to apply the fix with one click.
- Seamless code integration: Integrates with code repositories to generate pull requests for quick deployment.
SmartResolve 2.0 marks a significant leap forward in functionality, enabling mobile development teams to focus on innovation and delivering new features by minimizing time spent fixing bugs in their existing codebase.
Currently available as part of Instabug's Crash Reporting, SmartResolve 2.0 is in private beta for customers who opt to have their source code ingested by Instabug's AI model.
The Latest
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...