Use Codecov.

Author: khatchad

.github/workflows/continuous-integration.yml

@@ -49,9 +49,14 @@ jobs:
         mvn install -B -q -DskipTests
         popd
     - name: Build with Maven
-      run: mvn -Dlogging.config.file=./logging.ci.properties verify -B -Pjacoco coveralls:report -DrepoToken=$COVERALLS_REPO_TOKEN
+      run: mvn -Dlogging.config.file=./logging.ci.properties verify -B -Pjacoco
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        token: $CODECOV_TOKEN
+        slug: ponder-lab/ML
       env:
-        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
+        CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
     - name: Deploy with Maven
       run: mvn -s .travis.settings.xml -Dgithub.username=$GITHUB_USERNAME -Dgithub.password=$GITHUB_TOKEN deploy -DskipTests -B
       env:

.travis.yml

@@ -19,7 +19,7 @@ install:
  - mvn install -B -q -DskipTests
  - popd
 script:
- - mvn -Dlogging.config.file=./logging.ci.properties verify -Pjacoco coveralls:report -DrepoToken=$COVERALLS_REPO_TOKEN -B
+ - mvn -Dlogging.config.file=./logging.ci.properties verify -Pjacoco -B
 deploy:
   provider: script
   script: mvn -s .travis.settings.xml -Dgithub.username=$GITHUB_USERNAME -Dgithub.password=$GITHUB_TOKEN deploy -DskipTests

README.md

@@ -1,6 +1,6 @@
 # [Ariadne](https://wala.github.io/ariadne/)
 
-[![Continuous integration](https://github.com/ponder-lab/ML/actions/workflows/continuous-integration.yml/badge.svg)](https://github.com/ponder-lab/ML/actions/workflows/continuous-integration.yml) [![Coverage Status](https://coveralls.io/repos/github/ponder-lab/ML/badge.svg)](https://coveralls.io/github/ponder-lab/ML)
+[![Continuous integration](https://github.com/ponder-lab/ML/actions/workflows/continuous-integration.yml/badge.svg)](https://github.com/ponder-lab/ML/actions/workflows/continuous-integration.yml) [![codecov](https://codecov.io/gh/ponder-lab/ML/graph/badge.svg)](https://codecov.io/gh/ponder-lab/ML)
 
 This is the top level repository for Ariadne code. More information on using the Ariadne tools can be found [here](https://wala.github.io/ariadne/). This repository is code to analyze machine learning code with [WALA]. Currently, the code consists of the analysis of Python (`com.ibm.wala.cast.python`), analysis focused on machine learning in Python (`com.ibm.wala.cast.python.ml`), support for using the analysis via J2EE WebSockets (`com.ibm.wala.cast.python.ml.j2ee`) and their associated test projects.
 

pom.xml

@@ -267,27 +267,6 @@
         <activeByDefault>false</activeByDefault>
       </activation>
       <build>
-        <pluginManagement>
-          <plugins>
-            <plugin>
-              <groupId>com.github.hazendaz.maven</groupId>
-              <artifactId>coveralls-maven-plugin</artifactId>
-              <version>4.7.0</version>
-              <configuration>
-                <jacocoReports>
-                  <jacocoReport>${project.basedir}/com.ibm.wala.cast.python.report/target/site/jacoco-aggregate/jacoco.xml</jacocoReport>
-                </jacocoReports>
-              </configuration>
-              <dependencies>
-                <dependency>
-                  <groupId>javax.xml.bind</groupId>
-                  <artifactId>jaxb-api</artifactId>
-                  <version>2.3.1</version>
-                </dependency>
-              </dependencies>
-            </plugin>
-          </plugins>
-        </pluginManagement>
         <plugins>
           <plugin>
             <groupId>org.jacoco</groupId>