BURT: A task-oriented dialogue system for bug reporting

Overview

NOTE: the tag of the replication package for the FSE’22 artifact review is: BURT 1.1.0

This repository contains the replication package of our ESEC/FSE'22 paper:

Y. Song, J. Mahmud, Y. Zhou, O. Chaparro, K. Moran, A. Marcus, and D. Poshyvanyk, “Toward Interactive Bug Reporting for (Android App) End-Users,” in Proceedings of the 30th ACM Joint Meeting on the Foundations of Software Engineering (ESEC/FSE'22), 2022, to appear

This README will walk you through installing and using BURT.

This README also describes the content of our ESEC/FSE'22 replication package.

What is BURT?

BURT is a web-based task-oriented chatbot for interactive BUg ReporTing (BURT), which offers a variety of features such as the ability to:

• Guide the user in reporting essential bug report elements (i.e., the observed behavior or OB, expected behavior or EB, and steps to reproduce the bug or S2Rs);
• Check the quality of these elements, offering instant feedback about element issues (e.g., ambiguities); and
• Provide graphical suggestions (e.g., the next S2Rs at a given moment).

You can watch a video demonstration of BURT here.

BURT is implemented as a web application with two major software components: the backend server and the frontend (GUI). The backend server is implemented with Spring Boot (Java) and the frontend is implemented with the React Chatbot Kit (Javascript, Node.js). The frontend comunicates with the backend server via REST messages.

This figure shows an overview of BURT's workflow:

Building and deploying BURT

To build and run BURT on your computer, you need to:

1. set up the build/runtime environment,
2. build/run BURT's backend server, and
3. build/run BURT's frontend application.

NOTE: We provide instructions for installing BURT on Windows 10 and Mac OS Big Sur. Similar steps can be performed for other operating systems and other more recent versions of Windows and Mac.

1. Environment set-up

To set up BURT's build/runtime environment, complete the following steps:

NOTEs: make sure to install the version of the tools/frameworks as specified. Also make sure these can be executed in the terminal.

1. Install the Java Development Kit (JDK) 12:

   1. You can download OpenJDK 12.0.2 (build 12.0.2+10) from this website. Follow the instructions below, according to your operating system.
   2. Windows: download the Windows 64-bit zip file, unzip it, and move the decompressed folder (jdk-12.0.2) to an appropriate directory, e.g., C:\Program Files\Java\jdk-12.0.2. Then, add the JDK bin folder to the PATH environment variable (e.g., C:\Program Files\Java\jdk-12.0.2\bin). Finally, set the JAVA_HOME environment variable as described here (e.g., set it to C:\Program Files\Java\jdk-12.0.2).
   3. Mac OS: download the Mac 64-bit tar.gz file, extract the file and move the decompressed folder (jdk-12.jdk) to /Library/Java/JavaVirtualMachines. Run export JAVA_HOME=`/usr/libexec/java_home -v 12.0.2` to set the JAVA_HOME environment variable.
   4. If you have multiple JDK versions installed on your machine, make sure you use JDK 12.
   5. Verify the JDK installation by running java -version on the terminal. You should see the specific JDK 12 version you just installed.

2. Install the Maven 3.6.3 build tool. One way to do so is by downloading Maven from this website, decompressing the package, and adding the bin directory to the PATH environment variable. These tutorials explain other ways to install Maven: Windows or Mac OS.

   Verify the correct installation of Maven by runnnig mvn --version on the terminal.

3. Install the Node.js 15.8.0 runtime environment and its package manager npm 7.5.1:

   1. Windows: install the node version manager (nvm) by downloading its installer from here and then running the installer on your machine. Next, open a terminal with administrative rights, and run the following commands: nvm install 15.8.0 and nvm use 15.8.0. This website provides extra information about Node's installation on Windows.
   2. Mac OS: download Node's installer (node-v15.8.0.pkg) from here. Open the installer and follow the instructions on the screen.

   Verify the node/npm installation by running the commands: node -v and npm -v. You should see the version of the tools.

4. Install the Yarn 1.22.5 package manager:

   1. Windows: on a terminal with administrative rights, run: npm i --global [email protected]
   2. Mac OS: run sudo npm i --global [email protected] on the terminal.

   You can check Yarn is installed correctly by running yarn --version. You should see the version of Yarn.

5. Install Git (any version should work). A tutorial to install Git is found here.

6. By default BURT's backend server will run at the 8081 port and BURT's frontend will be accessible at http://localhost:3000. Make sure the ports 8081 and 3000 are available on your machine.

2. Building/running BURT's backend server

Complete the following steps:

1. Create an empty directory called fse-burt-project on your personal directory.

2. Download the installation script and save it in the fse-burt-project directory: Windows or Mac OS.

3. Open a terminal, go to the fse-burt-project, and run the build script:

   1. Windows: run run_server.bat.
   2. Mac OS: give permissions to the script (chmod +x run_server.sh) and then run the script (./run_server.sh).

   This step might take a while because the script will download all the dependencies needed to build BURT.

4. Once the script finishes, you should see on the terminal multiple messages from SpringBoot. The last message should look like this: "... Started ConversationController in 14.845 seconds...".

3. Building/running BURT's frontend

Complete the following steps:

1. Open a new terminal and go to the fse-burt-project/burt/burt-gui directory.

2. Run the script that builds/executes BURT's frontend:

   1. Windows: run the run_app.bat script.
   2. Mac Os: give permissions to the script (chmod +x run_app.sh) and then run it (./run_app.sh).

   This step may take a while because the script will download all the dependencies required by BURT's frontend.

3. At this point, the script should have executed successfully and your web browser should have opened showing BURT's graphical user interface (GUI). If the browser was not opened, open it and go to http://localhost:3000.

Reporting a bug using BURT

After you build/deploy BURT successfully, you can now report one or more bugs with BURT.

NOTE: We recommend you to watch a short demostration video that explains how to use BURT via an example; the video can be found here. Feel free to read our user guide to learn about BURT's GUI and how to use it.

To report a bug, you can enter P20 as participant ID, then select an app, and then report a bug using BURT's GUI. We provide 12 bugs from six Android apps (see more details in our original paper), the bug videos can be found here. Feel free to watch one bug video and report it using BURT.

ESEC/FSE'22 Replication Package

Our ESEC/FSE'22 replication package contains three main parts: BURT's source code, BURT's app execution data, and BURT's evaluation artifacts. These parts are composed of multiple directories/folders that we describe next.

BURT's source code

BURT's source code is found in multiple sub-folders (i.e., Maven packages) and mainly consists of three parts:

BURT's Front End

• burt-gui: this folder contains the frontend code that implements BURT's chatbot interface via the React Chatbot Kit and the Bootstrap framework. The chat-related folders (Chat, Chatbot, ChatbotMessage, etc.) contain the code about the chat box, and the StepsPanel folder has the code that displays the S2Rs that end-users have reported. The TipsOptions code implements the dynamic display of recommendations to end-users on how to use BURT.

BURT's Back End

• burt-server: this folder contains BURT's back-end server implemented with Spring Boot. It implements BURT's dialog manager and the REST-based web services that allow the front end to interact with different BURT sub-components. The back-end server is made up of several Java packages: actions, conversation, msgparsing, output, statecheckers. In the action package, you will find different actions that the chatbot will perform, such as asking users to select an app or provide an OB, EB, or S2R. In the conversation package, you will find different kinds of message objects and states, necessary for BURT to keep track of the conversation flow. The msgparsing folder contains all defined intents (i.e., types of messages) and the code that obtains the intent in each round of the bug reporting dialogue. The output folder contains the code that generates the web-based bug reports. The statecheckers folder contains the code that will check the end-user's message to decide the next action that chatbot will perform. The back-end server also implements the the S2R response predictor, which is in charge of suggesting to the user the next S2Rs that may be needed to be reported at a give moment.

• burt-quality-checker: this folder contains BURT's app execution model, which is a graph that stores the sequential GUI-level app interactions, and the dialogue quality processor, which checks the quality of the end-user's textual bug descriptions (i.e., the individual OB, EB, or S2Rs) and matches them to screens or transitions of the execution model.

• burt-nlparser: this folder contains BURT's natural language parser, which parses the textual bug descriptions (i.e., the invididual OB, EB, and S2Rs) provided by end-users, using dependency parsing via Stanford CoreNLP toolkit.

Tools for Generating App Execution Data

The following tools were used/implemented to generate the app execution data needed by BURT to build its app execution model.

• crashscope: this folder contains CrashScope's code, which generates app execution data in the form of sequential interactions (e.g., taps or type events) by utilizing a set of systematic exploration strategies on Android apps (e.g. top-down and bottom-up exploration). The code generates an screenshot of each interaction, XML files with GUI hierarchies, and app execution information for each screen of the app (e.g., which component was interacted with).

• AVT: this folder contains AVT, a custom screen-recording desktop app that we developed, which allows humans (e.g., end users or developers) to collect screen recordings and getevent traces from a mobile device or emulator by simply clicking a record button. The tool internally uses the Android screen recorder that is executed by Android’s ADB tool. The link to AVT is here.

• traceReplayer: this folder contains the code that processes the app interactions made by humans to execute app features (i.e., the traces collected via AVT). The code generates app screenshots, XML GUI hierarchy files, and app execution information used by BURT to complement/augment the execution model (i.e., the graph).

BURT's App Execution Data

The data directory contains the data used by BURT to build the app execution model. We describe the three main types of data stored in this directory:

• CrashScope data: The app exploration data that is collected by CrashScope, following multiple systematic app exploration strategies. We stored the screenshot, XML GUI hierarchy, and app execution information for each screen that was interacted with. This data is stored in data\CrashScope-Data.

• Crowdsourced app usage traces: We collected crowdsourced (i.e., human-based) usage traces from our six studied apps. These traces correspond to usages of the main features of the apps in our dataset. All the collected traces are stored in data\Collected_traces_fixed.

• TraceReplayer data: The screenshot, XML GUI hierarchy, and app execution information are extracted from each of the screens of the collected human-based traces. We stored the trace replayer data in data\TraceReplayer-Data.

BURT's Evaluation Data and Code

Our package provides the data that we used and collected for BURT's evaluation. It also provides the source code used to process this data and generate the evaluation results that we provide in our ESEC/FSE'22 paper.

Evaluation Data

• Bug videos and app installers: data\bug videos for evaluation contains the videos given to the participants that demonstrate the observed and expected app behavior for the 12 bugs we selected for evaluation. This folder also contains the app installers (i.e., APKs) that contain the bugs.

• Bug reports: the folder evaluation\data\generated_bug_reports stores all the bug reports generated by participants via BURT. The folder evaluation\data\itrac_bug_reports stores all the bug reports generated by participants via ITRAC (the template-based bug reporting system that we implemented via Qualtrics). Additionally, the Qualtrics reponses for ITRAC, in tabular format, are found in evaluation\code\ITRAC-study_March.xlsx.

• BURT conversations: the folder evaluation\data\conversation_dumps stores the conversations that each participant had with BURT.

• Questionnaire answers: evaluation\code\BURT Evaluation Survey_August-Anonymous.xlsx stores the participant responses to the online questionnaire that evaluated BURT's user experience.

Evaluation Code

• evaluation\code\bug_assignment.py contains the code that generated the random bug assignments to all participants.

• evaluation\code\qualtrics_data_analyzing folder contains the code that we used to process the results from the participant responses to the Qualtrics survey and generated the stacked bar charts that display the usefulness/ease of use of BURT's features.

• burt_result_analysis.xlsx, itrac_result_analysis.xlsx and create_analysis_spreadsheet.py in evaluation folder contain the code and xlsx that perform quality assessment analysis of the bug reports collected via BURT and ITRAC, see Table 3 and 4 in our original paper.

• evaluation\code\statistics_test folder contains the code that runs the statistical analysis for comparing bug report quality between BURT and ITRAC.