Despite advancements in automated defect triage, human judgment and intuition are irreplaceable in software development.
The themes of the 10 myths surrounding automated defect triage emphasize the indispensable role of human judgment, collaboration, and the complexity of software development, cautioning against over-reliance on automation and highlighting the need for continuous monitoring and adaptation in the defect management process.
Launchable helps teams optimize their defect management processes and accelerate software development workflows through intelligent automation.
Automation has always been a part of the future — remember the Jetsons, with all their fancy robots and smart houses doing everything for them? While we can’t quite get there yet, software developers have embraced automation with open arms, aiming to make development as smooth as possible. Organizations want to ensure their software is top quality — meaning that adding automation to their defect process has become incredibly important.
However, even with automation technology being fairly common within the last few years, there are still plenty of developers who don’t fully grasp how automation can help (or hurt) teams when it’s not used properly.
Automation is the future, and there’s no way to discount its importance to modern software development. And while it does boost efficiency during defect triage, it can’t fully replace human intuition and judgment.
Modern AI tools can identify (and sometimes even fix) issues during bug triage, but they can’t understand a product like your devs do. They lack the intuition, strategy, and context that only a real brain can recognize — making the human element crucial when making sensitive decisions or dealing with complex issues.
Bug triage automation can help streamline your defect management, but it can’t do all of the work for you. It’s an excellent method that can help streamline your processes and boost your team’s overall efficiency, though. Defect triage is a lengthy, complex process that can’t be fully solved by automating it all, and it can’t keep your team in the loop as time passes.
A critical part of your defect management process involves keeping your dev, QA, and DevOps teams in the loop regarding critical issues and progress as defects are worked on. Teams will still need to keep a close eye on data quality and validity, as well as monitor resources used in the process. Plus, teams will still need to meet, communicate, and collaborate on issues as the bug triage process progresses to ensure things align with business needs.
Remember what we just said about collaboration? It’s a critical part of defect management — teams must work together to identify, prioritize, and solve the bugs as they go along. Doing it alone is no way to achieve success, it’ll only lead to more issues if left unchecked.
This applies to defect triage automation, too. You can set up automation rules to run tests, return results, and resolve some issues, but it can’t do everything perfectly. A human element is still needed to create and fine-tune these rules, ensure test results are validated, and handle unique defects that automation can’t fully grasp without the complex knowledge a developer has.
While automation does speed up time-consuming tasks, it’s not on the same level as Vin Diesel and the power of family when it comes to being fast and furious. It can help speed up identifying, prioritizing, and testing, but it can’t always fix the issue. Additionally, it can’t always do all three of those at once — it’s a pretty big ask for an all-in-one solution.
In reality, developers still need to spend time fixing these issues and ensuring they are prioritized correctly with your established processes. Small issues can be fixed with automation, but most bugs can’t be solved without human intervention, especially more complex and severe defects.
You’d think that automation can completely solve classifying bugs as they appear in the discovery phase of defect triage. Automation tools can easily scan your reports and apply classifications to them as they’re added in, which can save your team's time, but they aren’t 100% accurate.
Software defects rarely play nice, and they aren’t always as simple as slapping a label on it and calling it a day. Automation tools aren’t perfect, and they can’t guarantee perfect accuracy. You’ll still need people to ensure that categories are assigned correctly with regular reviews.
Setting up automation rules can be a breeze, and you can very easily lay out the groundwork for several processes to be automated with ease (and some boilerplate). However, it’s not an all-in-one solution, not by a long shot.
Every project your team works on has its own needs and processes that need to be followed, which means you can’t just set it and forget it with automation tools. Your teams will need to drill down and explicitly figure out what rules you’ll need to put in place for your workflows and specific criteria for your business.
Automated tooling for triage will use the data it collects (and the data your teams and tests create) to work its magic. The triage process creates a lot of data — from tests, logs, results, and everything in between. If this data is left unattended, it can quickly send the quality of your automation tools down the drain.
If your data isn’t maintained, updated, and accurate, automation tools will keep doing their jobs, but with far worse results. These tools can easily spiral and throw errors or inaccurate results when they start using poor-quality data, which can wreak havoc on your entire triage process if it’s not caught early enough.
Adding in automation is supposed to make things more simple and streamlined. But when implemented poorly, it can be a massive detriment to productivity. A badly optimized process can stop your automated defect triage in its tracks, but that isn’t always the case.
When you take your time to properly integrate automation with a gentle guiding hand, it can be a massive benefit. Teams will be able to save massive amounts of time and resources with a proper setup in place, streamlining manual tasks and accelerating the entire triage process.
AI has been all the rage over the last year, from ChatGPT to Bard to college papers and practicing law. Automated triage has been impacted, too, with some tools adding AI to help out. However, this automation tech has been around longer than these AI tools.
It doesn’t take a team of AI-savvy engineers to set up and use automated defect triage tools. AI can be a huge boon to your automation, but it isn’t a die-hard requirement and doesn’t always have to be so complex. In fact, most tools offer user-friendly interfaces that don’t require a drop of AI knowledge.
As much as we’d all love for our processes and upgrades to be a set-and-forget system, it’s never that simple. You may think that adding an automation tool to your workflows is plug-and-play, but it couldn’t be farther from the truth.
Even with a streamlined defect triage process, it can’t run on its own without any supervision. You’ll still need to monitor it as time passes to ensure it continues running smoothly. Additionally, you’ll probably need to make adjustments in the future to stay aligned with project needs as it matures.
While we aren’t fully-fledged Mythbusters (we tried, but we couldn’t grow as cool of a mustache as Jamie Hyneman), we can definitely shine some light on some of these myths that come along with automating your defect triage. And with Launchable, you can take your bug triage to new heights, far beyond the realm of myths.
Turn your error logs from a slog to an action plan.
With Launchable’s Intelligent Test Failure Diagnostics, you can take those lengthy error logs and transform them into concise, intelligent glimpses into the issues plaguing your software and why. It helps determine how often these issues appear, classifying them and speeding up the process.
Take a step into the future with intelligent automation.
Our platform scans your tests (and their results) to give you the most information possible. We help keep your teams on top of their game by dynamically updating issues as they’re tested and give insights into every test run. Plus, we give you the context into why and how a test fails in the first place — and link it into your tech stack to ensure you have all the context you need to investigate further.