Over the last quarter, my team's integration of Black Duck has become increasingly hampered by alert fatigue stemming from what I am terming "common-name partial matches." The core issue appears to be the tool's propensity to flag components based solely on a string match between a common word in our internal codebase and a package name in its knowledge base, without sufficient contextual validation.
For example, we have an internal utility library we've named `core-utils`. Black Duck now consistently generates "partial match" findings against dozens of open-source packages containing the substring "utils," such as `spring-utils`, `apache-utils`, and `python-utils`. The match confidence is often listed as low, but these findings still populate reports and require manual triage. This creates a significant signal-to-noise problem.
Our current workflow involves a nightly scan of a monorepo containing approximately 2,500 discrete projects. The false positive rate for these common name matches has increased from an estimated 5% of total findings six months ago to nearly 40% currently. The operational cost is non-trivial. To quantify this, I benchmarked the triage time:
* **Precision Finding (e.g., exact match on `log4j-core 2.14.1`):** Triage time ~30 seconds (review license/security).
* **Common Name Partial Match (e.g., `core-utils` vs. `net.sf.utils`):** Triage time ~90-120 seconds (must analyze component function, validate it's not actually embedded, dismiss).
Extrapolated across hundreds of such findings per week, this consumes multiple person-hours that could be directed toward substantive, high-risk issues.
We've attempted to mitigate this through the following configuration adjustments, with limited success:
* Increased the minimum match confidence threshold in `bdscan` properties.
* Expanded our custom component catalog for internal projects.
* Implemented filename filters to exclude certain directories.
The underlying problem seems to be in the dissociation of the matching algorithm from software composition analysis (SCA) context. A string like "utils" or "common" has high entropy in a general text search but low significance in dependency analysis unless accompanied by dependency manifest evidence (e.g., an entry in `pom.xml` or `package.json`).
My questions to the community are thus:
1. Are you observing a similar inflation of low-value "partial match" findings, particularly for generically-named internal packages?
2. Has anyone developed a robust filtering rule or workflow within Black Duck (or a complementary script) that can algorithmically suppress matches based on:
* Lack of an associated dependency management file.
* Package name commonness (perhaps a denylist of overly generic terms)?
3. Is there a known method to adjust the weighting algorithm, so that matches derived from actual build artifacts (`.jar`, `.whl`, `node_modules`) are prioritized over raw source code string scans?
I am prepared to share anonymized snippets of our scan configuration and the resultant JSON output for comparative analysis if others are investigating this. The goal is to restore the tool's precision and preserve its value as an audit and compliance mechanism, rather than a source of manual clerical work.
Oh man, the *partial match* noise. It's like Black Duck heard "utils" and decided to throw the entire kitchen sink at you. We had the same agony with an internal event bus we called "bridge." The scans lit up like a Christmas tree matching every "bridge" package from JavaScript to obscure Perl modules. Triage felt like playing whack-a-mole with a teaspoon.
You're right about the operational cost, it's brutal. We eventually had to build a filter list just to mute the top 20 common word offenders, which feels like we're paying for a fancy hammer and then taping over the alarm bell ourselves. Have you looked at the confidence score thresholds? Cranking those up helped us a bit, but then you start sweating about missing the one real hit buried in the junk.
40% false positives is a huge time sink. Have you tried adjusting the match confidence thresholds to filter those low-confidence partial matches? I've heard some teams push it higher just to get a cleaner report.
Did you ever run into issues where raising the threshold caused you to miss something legit? I'm setting up similar scans and wondering about that trade-off.
That's a great question about the trade-off. Cranking up the confidence threshold does clean up the dashboard, but it can absolutely introduce blind spots. In my experience, the real risk isn't missing a major known vulnerability on a popular library, but rather obscuring newer, less common packages that slip in with lower confidence scores.
We found a middle ground by using the thresholds in conjunction with component exclusions for our own internal project names. This way, we suppress the persistent noise we know is harmless, but keep the sensitivity higher for everything else. Have you looked at whether your platform allows for granular rule sets based on component type or path?
Let's keep it real.
The path-based rule suggestion is spot on. We had some success using that to quarantine our internal `lib-` directories, so they're scanned but the findings auto-suppress unless there's a high-confidence match on a specific CVE.
But it creates a maintenance tail of its own. Every time a new internal project spins up with a common name, you have to remember to add its path to the exclusion list. We missed a new "data-connector" service for a month because no one updated the rules.
Has anyone automated that rule management? Feeding a list of internal repo names from your source control into the SCA tool's config?
> "The false positive rate for these common name matches has increased from an estimated 5% of total findings six months ago to nearly 40% currently."
That's a brutal jump. I don't have a monorepo that size, but I'm just starting to shop around for SCA tools for our small shop, and stories like this make me nervous. We only have maybe 30 internal services, but half of them have names like `payroll-core` or `invoice-utils` because we're not creative.
I'm wondering if you've looked at anything other than Black Duck? I've been reading about how some of the newer tools claim to do semantic matching or at least let you set up "known safe" name patterns in the config rather than post-hoc filtering. Since you're already deep in the triage hole, do you think the tool itself is the problem, or is this just the reality of any SCA that relies on string matching? I'm trying to figure out if I should just accept the noise or pick a different vendor from the start.