Intro to sbt-web

Jul 8, 20149 likes2,638 views

sbt-web is the foundation for all client-side concerns in Play 2.3. It offers a new asset pipeline that integrates with the existing JavaScript world.

Intro to sbt-web
Marius Soutier!
Freelance Scala Developer
@mariussoutier
Asset handling in Play 2.3

Motivation
Play’s client-side support not ﬂexible enough,  
too opinionated which technologies it supports
Can’t keep up with speed of innovation in the
JavaScript world (see JavaScript drinking game)
If you can’t beat ‘em, join ‘em 
Adopt Node APIs and make them run on the JVM at
full speed
Factor out asset handling into sbt plugins to build
community independent from Play

Enter sbt-web
sbt-web!
Project Layout!
Asset Pipeline!
Dependency Management
sbt-js-task!
Write sbt tasks that execute!
JavaScript using js-engine!
- But let’s focus on sbt-web ﬁrst -
sbt-js-engine!
Execute JavaScript in sbt!
Trireme / Node / Nashorn
sbt-web-driver!
Execute DOM-based JavaScript!
WebDriver

.sbt and .Scala build ﬁles
Build is a big collection of key-value pairs
Tasks are computed settings
Assign values via :=, +=, ++=
Settings can be scoped by project, conﬁguration, or task;
default conﬁguration is Global
Just Enough sbt
Setting[Task[T]]
Setting[T]
compile in Compile
compile in Test
name := “demo”

Default Project Layout
- Play overrides src/main to app -

public
Test Assets
Directories
Assets
Plugin
webTargetGlobal
source 
Directory
source 
Managed
resource 
Directory
resource 
Managed
nodeModule
Directory
nodeModule
Directory
public
source 
Directory
source 
Managed
resource 
Directory
resource 
Managed
nodeModule
Directory
webModule
Directory
webModule
Directory
webModules
Lib
staging
Directory

Test Assets
Tasks
Assets
assets
Global
node 
Modules
web 
Modules
webJars
reporter
pipeline
stage
assets
assets

Setting Dependencies
webTarget
staging
Directory
target
Global
target/
target/web/
target/web/stage/
Settings depend on other settings
Eases making small changes

Source Tasks
CoffeeScript
*.coffee
JavaScript
LESS
*.less
CSS
Compile sources to public assets
Analyze sources
Integrates with error reporting
src/main/assets/
target/
web/
public/
public in AssetssourceDirectory in Assets
sources in Assets resources-managed/
main/
resourceManaged in Assets

Asset Pipeline
Input
Assets
Name
Hashing
(digest)
GZip
Output
Assets
Tasks operating on assets sequentially
Each stage operates on mappings and passes to next one
Play’s Assets router is aware of both digested and gzipped assets
pipelineStages := Seq(digest, gzip)

sbt-coffeescript Convert CoffeeScript to JavaScript
sbt-jshint JavaScript linting
sbt-stylus CSS preprocessor
sbt-less CSS preprocessor
sbt-mocha Server-Side JS Testing
sbt-scalajs Convert ScalaJS to JavaScript (TODO)
Source Plugins

sbt-digest Fingerprinting
sbt-gzip Gzip assets
sbt-rjs RequireJS integration, minify JavaScript
sbt-uglify Minify/uglify JavaScript (without RequireJS)
Asset Plugins

WebJars
• Client-side dependency management, including
transitive dependencies
• Packed in Jars, resolved like normal dependencies 
by sbt 
libraryDependencies += "org.webjars" % "bootstrap" % "3.2.0"
• sbt-web auto-extracts WebJars to  
target/web/web-modules/lib
• Mapped to jsDelivr CDN automatically by Play’s
Assets controller

• Node Package Manager
• Becoming popular for client-side dependencies
• js-engine resolves and auto-extracts dependencies
deﬁned in package.json
• Disclaimer: Does not seem to work 100% yet
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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik

This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements. Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows. We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure. The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.

Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik

Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe. We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved? Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security. Talk from SciFiDevCon 2025 https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5