Decoding the Software Glitch: A Business Guide to Troubleshooting’s Intricacies

February 9, 2024

Ross Ross Gerring

Software development isn’t always smooth sailing. Glitches and bugs rear their heads, threatening functionality and user experience. Navigating these challenges is the art of troubleshooting, a fascinating mix of scientific methodology and artistic intuition. By understanding this process, business leaders can foster empathy, collaboration, and ultimately, better software.

The Detective’s Toolbelt: Standard Workflows and Beyond

Developers aren’t lost in a chaotic storm. They follow established workflows, using tools like:

  • Replication: Can the issue be consistently reproduced? This pinpoints the problem, preventing wild goose chases.
  • Logging & Debugging: Logs act as digital breadcrumbs, revealing code’s inner workings. Different levels offer varying detail, like adjusting a microscope’s zoom.
  • Version Control: Like a code time machine, it tracks changes, helping pinpoint when the issue emerged.
  • Testing: Manual and automated tests, the software’s guardians, constantly verify functionality, catching issues before they cause trouble. Caution: automated testing sounds fantastic – and it is if done well. But building and maintaining high quality automated testing systems can add dramatically to the time and cost of software development, initially and ongoing. Hopefully AI testing suites will help with this (see later).

But there’s no universal recipe. Each case demands adaptability and creativity. Sometimes, the solution comes from an unexpected angle, not just brute force coding.

The Danger of Assumptions: Why Starting Fresh Matters

Troubleshooting requires an open mind. Assumptions can be dangerous. The initial bug report might contain biases or incorrect information. Never take it as gospel. Start fresh, gather your own data, and don’t jump to conclusions.

Reproduction is Key: Why It Matters Most

Imagine trying to fix a car you’ve never seen. It’s impossible. The same applies to software bugs. If a developer can’t reproduce the issue themselves, troubleshooting is close to impossible. If this happens, their priority becomes finding someone who can (e.g., the reporter) and working together to understand the specific environment and setup where the issue occurs.

In many ways, being able to successfully reproduce a problem is 80% towards being able to resolve it.

Beyond Technical Prowess: The Troubleshooting Persona

While technical skills and experience are important, successful troubleshooters often possess:

  • Curiosity: An insatiable desire to understand the “why,” not just the “how” behind the issue.
  • Logic & Patience: Methodical analysis coupled with the ability to stay calm under pressure, for troubleshooting can be a marathon.
  • Keen Eyes: Spotting subtle clues within vast data, akin to finding a gem in sand.
  • Communication: Bridging the gap between technical complexities and layman’s understanding, ensuring everyone’s on the same page. Importantly, this includes the need to clearly document the steps taken in the pursuit of a solution. This allows others working in partnership, or in the future, to learn from effort already expended, and more effectively adapt or re-test accordingly.

Interestingly, not all top troubleshooters are coding virtuosos. Some excel at lateral thinking, proving innovation often comes from unexpected corners.

The Ecosystem Matters: Beyond the Code Itself

The “needle in the haystack” often isn’t confined to the code itself. Consider these complexities:

  • Environments: Hardware, software, and network configurations are intricate ecosystems. A seemingly unrelated change in one corner can trigger issues elsewhere.
  • Third-party dependencies: Modern software relies on external libraries and integrations, which can introduce unforeseen glitches. Imagine building a house with faulty bricks.
  • Scale & Complexity: With millions of lines of code, modern software is like a sprawling city. Understanding how components interact is crucial for pinpointing the issue.

Therefore, effective troubleshooting demands a holistic view, considering the software within its broader ecosystem.

Empowering the Journey: The Toolbox of a Modern Detective

Developers aren’t alone in their quest. A vast arsenal of tools assists them:

  • Debuggers: Imagine stepping through code line by line, like rewinding a movie. Debuggers offer this view, pinpointing the exact error-causing line.
  • Profilers: These tools act like performance monitors, identifying bottlenecks that might be contributing to the issue. Think of them as traffic analysts highlighting congested roads.
  • Static code analysis: Proactive is always better than reactive. Static code analysis tools scan code for potential issues before they manifest as bugs, acting as preventive medicine for software.
  • IDEs: These advanced workspaces are the developer’s command center. They offer intelligent code completion, debugging aids, and context-aware suggestions, akin to having a helpful assistant whispering solutions.

These tools empower developers to navigate the intricate web of code and data, shedding light on the hidden pathways within the software labyrinth.

AI’s Rise in the Debugging Dungeon: How it Assists and Will Shape Troubleshooting

Software troubleshooting can be a time-consuming and complex endeavor, requiring both technical expertise and detective-like intuition. Fortunately, Artificial Intelligence (AI) is increasingly entering the debugging dungeon, offering developers valuable assistance and promising future advancements.

AI Chatbots

Chatbots like ChatGPT and Google’s Bard can provide direct conversational assistance to help troubleshoot issues. Both developer and non-technical persons alike can describe the issue being experienced, including sharing (if available) any error reporting they can see, and chatbots can sometimes be quite effective in suggesting the most likely causes of the issue, and recommended  next steps towards a resolution. The success or failure of various attempts can be described back to the chatbot, thereby allowing it to adaptively improve its advice.

Current Applications of AI in Troubleshooting:

  • Pattern Recognition: AI algorithms excel at sifting through vast amounts of data, particularly logs and code, identifying anomalies and patterns humans might miss. This “superpowered data detective” can pinpoint potential root causes much faster, saving developers precious time.
  • Automated Root Cause Analysis: Based on historical data and identified patterns, AI can suggest potential causes for issues, offering developers educated guesses to explore. Think of it as having a constantly learning consultant whispering possibilities in your ear.
  • Predictive Maintenance: Similar to checking your car for issues before they arise, AI can analyze system behavior and predict potential problems before they impact users. This proactive approach prevents downtime and ensures smoother software operation.

Beyond Current Capabilities: AI’s Future Potential:

  • Explainable AI: One of the current challenges with AI is understanding its reasoning. Future advancements in explainable AI will offer developers clearer insights into why the AI suggests specific solutions, fostering trust and collaboration.
  • Context-Aware Troubleshooting: AI models trained on diverse datasets can consider the specific context of an issue, including user environment, historical data, and code dependencies. This context-awareness will lead to more accurate and personalized troubleshooting solutions.
  • Self-Healing Systems: In the future, AI-powered systems might even be able to automatically diagnose and fix minor issues themselves, reducing the reliance on human intervention for basic troubleshooting tasks.

Benefits and Considerations of AI in Troubleshooting:

  • Increased Efficiency: By automating repetitive tasks and providing faster insights, AI can significantly speed up troubleshooting, leading to quicker bug fixes and reduced development costs.
  • Improved Accuracy: AI’s vast data processing capabilities can identify subtle patterns and connections humans might miss, potentially leading to more accurate diagnoses and efficient solutions.
  • Human-AI Collaboration: It’s important to remember that AI is not a replacement for human expertise. Its true value lies in assisting developers, not replacing them. The ideal scenario is a collaborative approach where humans leverage AI’s strengths while bringing their own judgment and creativity to the table.


Software development is a fascinating journey, and troubleshooting is a crucial part of it. By understanding the challenges, methodologies, and tools involved, business leaders can foster empathy, support, and ultimately, contribute to building better, more robust software. Remember, it’s not just about fixing bugs; it’s about collaboration, open-mindedness, and the collective pursuit of excellence.