Launch of SCAIFE System Model 2.0.0 Offers Help for Steady-Integration (CI) Programs


The Supply Code Evaluation Built-in Framework Setting (SCAIFE) system is a analysis prototype for a modular structure. The structure is designed to allow all kinds of instruments, methods, and customers to make use of synthetic intelligence (AI) classifiers for static-analysis outcomes (meta-alerts) at comparatively low price and energy. SCAIFE makes use of automation to cut back the numerous handbook effort required to adjudicate the massive variety of meta-alerts that static-analysis instruments produce. In June 2021, we launched Model 2.0.0 of the complete SCAIFE system with new options for working with continuous-integration (CI) methods. On this weblog submit, I describe the important thing options on this new launch and the standing of evolving SEI work on SCAIFE.

SCAIFE for Steady Integration

Steady integration (CI) has historically been outlined as “the observe of merging all builders’ working copies to a shared mainline a number of instances a day” and often contains automated builds and checks by a CI server. In a earlier SEI weblog submit, Steady Integration in DevOps, C. Aaron Cois wrote,

This continuous merging prevents a developer’s native copy of a software program challenge from drifting too far afield as new code is added by others, avoiding catastrophic merge conflicts…If a failure is seen, the event staff is anticipated to refocus and repair the construct earlier than making any extra code adjustments. Whereas this will appear disruptive, in observe it focuses the event staff on a singular stability metric: a working automated construct of the software program.

We have now enabled the SCAIFE system to work with a spread of variations of CI, together with these which might be broader than every day merges of developer branches on a shared server and CI-server automated testing. CI can vary from every day merges by builders after they commit adjustments to a code repository server, to much less frequent merges. An instance of a much less frequent merge is when a CI construct routinely checks developer branches on the CI server till all checks go, and solely after that the developer department is merged into the mainline department, which could for instance take every week or longer. We additionally allow testing by improvement organizations that don’t even use a CI server but, however which have generated totally different variations of the codebase and the static-analysis device output on each variations of the codebase. This method is a much less automated model of updates {that a} CI server supplies to SCAIFE routinely, and that SCAIFE makes use of to replace its challenge info, together with details about static-analysis outcomes.


Model 2.0.0 of SCAIFE contains 4 variations of a hands-on CI-SCAIFE integration take a look at that demonstrates options that allow SCAIFE to work with CI methods. The variations differ by the quantity of automation that the tester is ready to use. For instance, some testers might not have a CI server out there or might not have sufficient out there time (a few half day) to run the complete CI-server model of the demo. Customers create a SCAIFE CI challenge utilizing a git code repository, the place a brand new code decide to the code repository routinely causes an replace—code commit and new static-analysis device output—to be despatched to the SCAIFE DataHub module (see Determine 1 beneath), which processes the CI replace.

Determine 1 beneath exhibits the structure of the SCAIFE system with modifications for CI-SCAIFE integration:


Determine 1: Modified SCAIFE Structure for Integration with a CI System

Determine 2 beneath exhibits a imaginative and prescient that integrates classifier use with CI methods.


Determine 2: Integration of Classifier Use with CI Programs

Determine 2 exhibits CI workflow, the place a member of the event and take a look at groups develops code on a brand new department (or branches) that implements a brand new characteristic or a bug repair. The coder checks their supply code into the repository (e.g., git commit and git push). Subsequent, the CI server checks that code, first organising what is required to run the checks (e.g., creating information and folders to report logs and take a look at artifacts, downloading pictures, creating containers, working configuration scripts), then beginning the automated checks. Within the quick CI timeframes, important checks should be run, together with

  • unit checks that verify that small bits of performance proceed to work,
  • integration checks that verify that bigger components of the system performance proceed to work together as they need to, and typically,
  • stress checks to make sure that the system efficiency has not develop into a lot worse.

Typically (however not all the time) static evaluation is finished throughout the CI-server testing. When this take a look at happens, it produces output with many alerts. Some meta-alerts could also be false positives, and all of them should (usually) be examined manually to adjudicate true or false positives. In very quick CI timeframes, nonetheless, coping with static-analysis alerts is of low precedence for improvement and take a look at groups. Any failed unit or integration take a look at should be mounted earlier than the brand new code department may be merged with the event department, so these are of excessive precedence. Past that, there are main time pressures from the CI cycle and the opposite builders or testers who want that bug repair or a brand new characteristic added so it doesn’t block their very own work or trigger a merge battle sooner or later.

To utilize static evaluation sensible throughout quick CI builds, we

  • enabled diff-based adjudication cascading within the SCAIFE DataHub, static-analysis classification that automates the dealing with of some outcomes;
  • enabled the person to set thresholds for classification confidence, above which the outcomes are thought of excessive confidence and beneath which they’re thought of indeterminate; and
  • described a technique by which customers can deal with a small variety of code-flaw circumstances throughout CI builds briefly time frames. At different CI levels, customers can widen such a set of code flaws and nonetheless use SCAIFE, in a static-analysis adjudication course of that takes extra time to deal with a wider vary of potential code-flaw circumstances reported from static-analysis outcomes.

The DataHub Module API supplies a CI endpoint that automates evaluation utilizing SCAIFE if a package deal is configured to make the most of CI integration. Configuring a package deal for CI integration signifies that the DataHub Module will instantly hook up with a git-based version-control system to research the supply code used within the SCAIFE software. After static evaluation runs on the supply code, the outcomes are despatched to the DataHub API to start automated processing with SCAIFE.

The DataHub updates per-project knowledge, together with all details about information and features; the units of static-analysis alerts and meta-alerts; and adjudication cascading. Adjudication cascading includes matching static-analysis outcomes from the earlier code model with new static-analysis outcomes for the brand new code model. An identical meta-alert “cascades” any earlier handbook adjudication of true or false to the brand new meta-alert and sends the up to date challenge knowledge to the SEI CERT Division‘s SCALe (Supply Code Evaluation Laboratory) device. SCALE is a graphical person interface (GUI) entrance finish for the SCAIFE system (proven on the prime of Determine 1 above) that auditors use to export project-audit info to a database or file.

We offer variations of the demo checks, to allow customers to run the kind of CI-SCAIFE demo take a look at acceptable to their methods and testers. The totally different take a look at model directions are for testers who

  • have their very own CI methods and git code-repository servers,
  • have git code-repository servers however no CI, or
  • do not have entry to a CI or git code-repository server.

There are 4 several types of demos that customers can run:

  1. If the person has a CI system and desires to totally train the demo, this model contains use of a CI server, a git repository, and the Rosecheckers static-analysis device.
  2. For customers who’ve only some minutes, that is the quickest demo, the place a script does many of the steps: Customers comply with the steps proven right here: Demo with Fully Automated Demo Script. The script makes use of the preset knowledge with two code variations and Rosecheckers output that’s supplied for every model of supply code, and the script itself creates a neighborhood git repository. Code-terminal output explains the importance of what occurs at steps of the demo, verifies counts of meta-alerts for each variations of the codebase, and explains the adjudication-cascading outcomes.
  3. To train the quickest non-scripted demo requiring the smallest quantity of effort, customers use the preset knowledge at Demo with out utilizing a CI Server and comply with Strategy 1 utilizing the Rosecheckers output that’s supplied for every model of supply code. This method has the person edit a supplied shell script, to specify a token, URL, git commit hash, and different knowledge gathered throughout specification of the CI Venture in SCAIFE whereas following the directions. The person then executes the shell script.
  4. To train the second-fastest non-scripted demo requiring a bit extra effort than (3), customers use the preset knowledge in Demo with out utilizing a CI Server and comply with Strategy 2 utilizing the Rosecheckers output that’s supplied for every model of supply code. This method makes use of the DataHub container’s Swagger person interface, plus the static-analysis outcomes, to submit static-analysis outcomes to SCAIFE.

In creating the demos, we additionally printed the Docker container picture for the Rosecheckers static-analysis device (accessible with command-line set up command: docker pull and the code at, with an up to date README file. Our challenge staff created the brand new Docker-container-image publication, which allows would-be customers to shortly and simply begin to use Rosecheckers with a comparatively low-bandwidth obtain and quick container begin on any base machine. We printed it to allow this tooling to be straightforward to entry and arrange as quick as doable, for our collaborators to run and take a look at some variations of our SCAIFE-CI demo extra shortly.

Standing of Launch and Deliberate Subsequent Steps

We’re within the technique of sharing the complete SCAIFE system with DoD organizations and DoD contractors in order that we are able to obtain suggestions and evaluation. We additionally present entry to SCALe—considered one of 5 SCAIFE modules, the user-interface (UI) module, and the SCAIFE API, to most of the people at We welcome take a look at and evaluation suggestions, in addition to potential collaborations! DoD and DoD contractor organizations inquisitive about testing SCAIFE, please contact us and we’ll get you a replica of the complete SCAIFE system.