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
While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...
Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...
For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...
For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...
Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...
Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...
Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...
Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...
AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...
More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...