When Logs Become Stories – How the Next Generation of Debugging is Evolving

Published on 26.12.25

The industry is seeking new methods to comprehend the behavior of systems. In this interview with a Tech Lead at a large-scale company, Dmitrii Volykhin explains how automatic analysis and accurate power measurements are changing device debugging and setting a new standard for development.

In recent months, the development industry has faced a dramatic increase in the complexity of observability and debugging processes: in October 2025, Google introduced a new Logs & Datasets feature in AI Studio, a tool that allows you to automatically log API calls and errors without modifying code, reinforcing a global trend: traditional debugging tools can no longer keep up with the volume and variability of modern systems. Research supports this shift: the 2024 HELP study (Hierarchical Embeddings-based Log Parsing) showed that classic log template parsers fail even with minor format changes, making them unsuitable for large ecosystems like Android.

Against this backdrop, the industry increasingly demands tools that can understand system behavior, identify root causes, and route issues automatically. Dmitrii Volykhin works precisely at this intersection of log scalability challenges and the emerging discipline of behavior-driven debugging. While much of the market is still discussing “intelligent observability” in theory, his work focuses on building practical tools that translate Android Bugreports and Logcat analysis from manual investigation into automated system understanding.

This interview is about the emergence of a new engineering discipline: from checking logs with grep, to systems that interpret Android behavior as a coherent story. Together with Dmitrii, we talk about why the old methods have stopped working, what “AI-log understanding” is, and why it will soon become the standard for high-load systems.

– Dmitrii, how do you assess the trend that research groups and developers are talking about today: traditional log analysis is no longer working, and systems are becoming too complex for old tools, is that true?

– This trend is absolutely real. We see several factors at once. Firstly, modern systems have become too dynamic. Any small update changes the format of logs, the sequence of events, and the names of processes. Any template parser breaks down from such trifles – as soon as the structure of events shifts, the tool loses stability and can no longer extract meaning from the data.

Secondly, the devices are no longer monofunctional. Now, even a small wearable is a full-fledged distributed system: camera, sensors, radio modules, ML processes, Android services. All this generates a considerable amount of telemetry. And the more smart features are added to devices, the faster the volume of Bugreports grows, up to hundreds of megabytes of raw information.

Thirdly, the world really needs to make this transition from text parsing to understanding behavior. The industry is moving away from the idea of “reading logs with your eyes” to the concept of “understanding what the system is doing and why”. In this sense, the log is no longer a string, but a story. What is happening today is very natural: the old tools cannot withstand the scale. And AI becomes the natural next step, because only AI can handle such complexity and the amount of data.

– You said that the log is now a story. How can an engineer understand such a story in the face of an increasing volume of data? What are the key principles of analysis?

– The most important thing is to stop thinking of logs as a set of unrelated strings. When a system becomes complex, the log turns into a sequence of events that describe its behavior. To understand this, an engineer needs to look specifically at the context and dynamics, rather than at individual messages. First of all, it is important to analyze sequences, not only single records. For example, it is important not just to see the error, but to understand what preceded it. In addition, the engineer must understand the baseline – how the system behaves in a “healthy” state. Any deviation becomes clear only in comparison. This is especially important for large Bugreports, where it is impossible to assess with your eyes what looks wrong. In fact, log analysis turns from a search for textual matches into a task of interpreting the behavior of the system. This is the new level of complexity that we are seeing in the entire industry today.

– And here comes your idea of error analysis that automatically extracts battery insights directly from bug reports during End-to-End performance testing in the laboratory –  Performance Detection platform. When did you first realize that without automation, it could not scale any further?

– The turning point was working with complex smart devices. When you see a Bugreport with hundreds of megabytes, you realize that no one can figure it out fast enough to match the pace of development. Plus, smart devices are an environment where every little thing affects the outcome: any wrong decision can cost an hour of battery life. Therefore, it was necessary to understand every detail of the system’s behavior – and understand it fast. The development process is unique because it treats bugs as a collection of signals. By automatically extracting time series – or any metric indicating a shift in device behavior – the system identifies battery life regressions immediately. This automation allows for the resolution of critical issues long before they reach production.

 – Your development is responsible for the correct operation of the battery. You talk a lot about the connection between logs and power consumption. Why is this so important for smart devices?

– Because power consumption is both a pain and a quality indicator for smart devices. Smaller devices don’t have any power reserves: everything works on a budget of less than 1 watt. Any event in the logs – sensor awakening, a short CPU spike – costs real milliwatts. And milliwatts are minutes on a battery. Therefore, we cannot consider logs in isolation. You need to see how they affect the physical behavior of the device.

– Did this logic lead to the creation of the Power Context Aware Tool, which made it possible to measure power with a single command? 

– Yes, that’s right. The need to understand the behavior of the device not only at the log level, but also at the physical level: how much energy each subsystem actually consumes, directly led to the development of the Power Context Aware tool (PCAT). Prior to this, engineers relied on external power meters, which cost over 1,000 pounds per device and required manual setup, cables, scripts, and hours of supervised testing. It was an accurate solution, but it just didn’t scale. PCAT has completely changed this. Over time, this led to the emergence of tooling that allows engineers to run power measurements with a single command. No special equipment, no manual intervention. This initiative slashed testing costs from £1,000 to under £10 per run. By enabling every test to function as a performance test, coverage reached 95%.

– Can we say that this approach gradually changes how engineering teams think about logs and power consumption?

– I can’t speak for all Android engineers, but I suppose so.  Logs have become a source of truth about the device’s behavior. The engineers saw a causality between the actions of the system: what wakes up the radio, what causes unnecessary wakeups, and why consumption is increasing. It changes the mindset – for the better. Power and performance have ceased to be the task of several specialists – it has become the task of each team.

– Your mission as a Developer and Tech Lead is also to simplify the work of the team and transfer experience. You speak at conferences, have a podcast about Java, which has amassed over 5,000 followers and more than 10,000 plays on platforms such as Apple Podcasts, Google Podcasts, and Spotify. How did the desire for help and training begin?

– It began just when I started the Javaswag podcast. I wanted to give a platform to talented engineers, especially from regions and undervalued teams. Now the podcast has tens of thousands of listeners – it’s a whole community. Then FaangTalk appeared, a YouTube channel and Telegram chat where I help engineers prepare for Big Tech interviews. During this time, hundreds of people have received offers from the largest companies in the world, like Amazon, Google, and Uber. I believe that knowledge should be accessible. AI tools are cool, but it all starts with people.

– Your contribution to the training helps to form a new understanding in the industry. Looking ahead, how do you see the evolution of log analysis in the next 5 years?

– I am convinced that in five years, 80% of the log analysis will be performed by AI. The log will cease to be a file – it will become a model of system behavior. The tools will automatically identify the causes, suggest fixes, and prevent regressions. We are only at the beginning of this transformation, but we can already see how it is changing development. And I am sure that the debugging of the future will not be based on sympathy for “grep”, but on understanding systems as living organisms.