Having extensively analyzed the cost structures of various software supply chain tools, I've observed a significant operational tax that is often overlooked: the resource expenditure required to triage and validate security and license findings. This brings me to the core of my inquiry regarding Black Duck.
Our organization is conducting a thorough Total Cost of Ownership (TCO) analysis for Software Composition Analysis (SCA) solutions. While the licensing cost of Black Duck is a clear line item, the indirect costs associated with managing false positives present a substantial, often unquantified, operational burden. I am seeking detailed, concrete feedback from engineering and security teams who operate Black Duck in production environments.
Specifically, I am interested in empirical data and workflow experiences related to:
* The baseline false positive rate for common vulnerability patterns (e.g., vulnerabilities in unused code paths, version mis-matches, transitive dependencies flagged incorrectly).
* The average time expenditure per developer, per week, to investigate and dismiss non-actionable findings. This is a critical resource allocation metric.
* Configuration and tuning strategies you have employed to improve signal-to-noise ratio. Please share specific examples of policy adjustments, component matching criteria, or suppression rules.
```xml
CVE-2023-12345
commons-io:commons-io
[2.0,2.5)
```
* The comparative effectiveness of Black Duck's proprietary scanning versus integrating with other scan data (e.g., direct CycloneDX SBOM import). Does one method yield materially better accuracy?
* Any quantifiable impact on pipeline velocity. Have you needed to introduce lengthy review gates or dedicated FTE resources to manage the output?
From a FinOps perspective, a tool that generates excessive noise directly translates to inflated engineering costs, delayed releases, and potential alert fatigue that leads to critical issues being overlooked. I am less interested in marketing claims of coverage breadth and more in the operational efficiency and precision of the platform. Your practical insights will be invaluable for building a realistic cost-benefit model.
- cost_cutter_ray
Every dollar counts.
You've hit on the exact problem that pushed us off Black Duck two years ago. The operational tax wasn't just the triage time, it was the alert fatigue that caused real issues to be missed.
Our data for a ~500 service k8s environment: we measured about 40% of vuln findings required manual investigation to confirm as false. The biggest time sink wasn't the version mismatches, it was the findings in test scopes and build-only dependencies that the tool couldn't correctly contextualize. We tracked it at about 2-3 hours per developer, per sprint, just on validation.
The configuration to reduce noise became a full-time job itself. You can build suppression files and curate custom KBs, but then you're maintaining a shadow system. The break-even for us was when the cost of that internal suppression framework maintenance surpassed the license cost of a more contextual tool.
Wow, that 2-3 hours per developer per sprint metric is really sobering. We're evaluating SCA tools now and trying to forecast internal costs, and that's exactly the kind of tangible data we need.
>the configuration to reduce noise became a full-time job
This is my biggest fear. As a smaller team, we just don't have the cycles to maintain what amounts to a second, custom rule system on top of the tool we're paying for. It sounds like the tool's inability to understand context, like test dependencies, creates a massive technical debt of its own.
Can I ask what you moved to, and did the contextual understanding actually work better, or did you just trade one set of problems for another?
> I am seeking detailed, concrete feedback
I wish I had hard numbers for you. We're just starting our POC with Black Duck now, and quantifying this operational tax is precisely why I'm in this thread. It's the hidden cost everyone warns about.
Our main worry is the license compliance side. Could the noise from false positives in that area actually create more risk if teams start ignoring alerts? How do you even measure that in a TCO?
Still learning.
That context issue with test dependencies is a real headache. I'm just getting our docker-compose setups scanned and I'm already seeing noise from dev-only containers. 😕
>what you moved to
I'd love to hear that too. For smaller teams, is the answer just a simpler tool, or do all the big SCA platforms have this same problem?
Containers are magic, but I want to know how the magic works.