SATD refers to self-admitted technical debt, which is introduced intentionally (e.g., through temporary fix) and admitted by developers themselves and always recorded in source code comments. SATD Detector[1] is a tool that is able to automatically detect SATD comments text mining. This is the back-end of SATD Detector, which provides command-line interface and Java API of SATD Detector. More details can be found in [1].

Download binaries: satd_detector.jar

Use build-in models to test whether a comment is SATD comment or not:

$ java -jar satd_detector.jar test
>This is an ugly implementation.
SATD
>This function read lines from file.
Not SATD
>/exit
bye!

To train your own models, you need provide three data files:

• Comment file: contains all the comments in your dataset. One line for each comment.
• Label file: contains the labels of comments.
• Project file: contains the project names of comments.

Comment file example: comments.txt

// This is comment 1
// TODO: fix this function later
/* This is comment 3 */

Label file example: labels.txt

No
Yes
No

Project file example: projects.txt

project1
project2
project3

Train your own model

$ java -jar satd_detector.jar train -comments comments.txt -labels labels.txt -projects projects.txt -out_dir ./models/
Finish Saving classifier of project1
Finish Saving classifier of project2
Finish Saving classifier of project3

Now you can see 9 files in ./models/:

- models
    - project1.as
    - project1.model
    - project1.stw
    - project2.as
    - project2.model
    - project2.stw
    - project3.as
    - project3.model
    - project3.stw

All these files will be loaded while classifying.

$ java -jar satd_detector.jar test -model_dir ./models/
>This is an ugly implementation.
SATD
>This function read lines from file.
Not SATD
>/exit
bye!

Your can also use SATD Detector in your Java projects.

• Download satd_detector.jar
• Add it to the classpath

import satd_detector.core.train.Train;

String commentFile = "./comments.txt";
String labelsFile = "./labels.txt";
String projectsFile = "./projects.txt";
String outDir = "./models/";

// train your own models
try{
    Train.buildModels(commentFile, labelFile, projectFile, outDir);
} catch (Exception e) {
    e.printStackTrace();
}

import satd_detector.core.utils.SATDDetector;

// create an instance using build-in models
SATDDetector detector1 = new SATDDetector();
String comment = "This is an ugly implementation."
boolean result = detector1.isSATD(comment)

// create an instance using your own models
String modelDir = "./models/"
SATDDetector detector2 = new SATDDetector(modelDir);
boolean result = detector2.isSATD(comment)

$ java -jar satd_detector.jar test -h
usage: test
 -h                 Show help message
 -model_dir <arg>   Dir which stores all the models. Using build-in models
                    if not specified.

$ java -jar satd_detector.jar train -h
usage: train
 -comments <arg>   The file which stores all comments.
 -h                Show help messages.
 -labels <arg>     The file which stores labels of all comments.
 -out_dir <arg>    Dir to store trained models.
 -projects <arg>   The file which stores projects of all comments.

• weka 3.8.1 or above
• snowball-stemmers
  • which can be installed using weka package manager
• Apache commons cli 1.4 or above

• Git clone or download zip
• Add resources folder to your classpath
• Add weka.jar, snowball-stemmers.jar and commons-cli.jar to your classpath

For Eclipse, you can modify classpath quickly by:

• Righ-click on the project
• Choose Build Path -> Configure Build Path
  • for resources folder, choose Source tab -> Add Folder
  • for jar files, choose Libraries tab -> Add External JARs

[1] SATD Detector: A Text-Mining-Based Self-Admitted Technical Debt Detection Tool.