<dependencies>
    <dependency>
      <groupId>test</groupId>
      <artifactId>a</artifactId>
      <version>0.2</version>
      <scope>system</scope>
      <systemPath>should-use-variables-and-not-hard-code-this-path</systemPath>
    </dependency>
    <dependency>
      <groupId>test</groupId>
      <artifactId>b</artifactId>
      <version>0.1</version>
      <scope>system</scope>

        // TODO: hard coded string
        return create(DependencyCoordinatesFactoryRequest.build(
                session, plugin.getGroupId(), plugin.getArtifactId(), plugin.getVersion(), null, null, "maven-plugin"));
    }

    @Nonnull
    default DependencyCoordinates create(@Nonnull Session session, ReportPlugin reportPlugin) {
        // TODO: hard coded string

docs/debugging/hash-set/hash-set
docs/debugging/healing-bin/healing-bin
docs/debugging/inspect/inspect
docs/debugging/pprofgoparser/pprofgoparser
docs/debugging/reorder-disks/reorder-disks
docs/debugging/populate-hard-links/populate-hardlinks
docs/debugging/xattr/xattr
hash-set
healing-bin
inspect
pprofgoparser
reorder-disks
s3-check-md5
s3-verify
xattr

We use *Specification* and *Discovery* documents stored in Google Drive, but they present some downsides:

* They are rarely updated after creation and initial review, and then become hard to follow, especially after important decisions are made
* They are not synced with the code to reflect the eventual solution that is committed
* Google Docs is not a "code-oriented" tool, like asciidoc can be

   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time

   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time

   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard
   * about arrays for now, as they're a mess. (We previously discussed this in the review of
   * ObjectArrays, which is the main caller of this method.)
   */

   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard
   * about arrays for now, as they're a mess. (We previously discussed this in the review of
   * ObjectArrays, which is the main caller of this method.)
   */

                        }
                    }
                }
            }
        }
    }
}

// run the parallel ITs
parallel(runITsTasks)

// JENKINS-34376 seems to make it hard to detect the aborted builds
} catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e) {
    echo "[FAILURE-002] FlowInterruptedException ${e}"
    // this ambiguous condition means a user probably aborted

		}
	}
	return nil
}

// bitrotSelfTest performs a self-test to ensure that bitrot
// algorithms compute correct checksums. If any algorithm
// produces an incorrect checksum it fails with a hard error.
//
// bitrotSelfTest tries to catch any issue in the bitrot implementation
// early instead of silently corrupting data.
func bitrotSelfTest() {
	checksums := map[BitrotAlgorithm]string{