Introduction

Bugs are inevitable in software products. The rising complexities in modern software solutions have made fixing bugs more challenging than ever before.

Finding bugs and fixing them is a tedious and time-consuming process. It’s very much possible that an error occurring in a code base might be due to a small syntax error that went unnoticed before and many similar reasons. Other reasons include the repetitive and complex nature of code making them prone to human errors, the introduction of new bugs while fixing others, and the not-so-easy part of reproducing them efficiently when the QA engineers report them to the developers.

The cost incurred by these errors in software products is no joke.

As per the press release in December 2022, by the Consortium of Information & Software Quality or CISQ, the cost of poor software quality in the US for the year 2022 had grown to a colossal USD 2.41 trillion.

Another interesting data released in the same report was the accumulated cost of technical debt in software touching the figure of USD 1.52 trillion in the year 2022. Software technical debt reflects the cost incurred while reworking software for optimization and resolving issues.

It’s not surprising to see a whole lot of bugs that could have been avoided with efficient bug management methods. The fact that they still find their way into a product code may be due to a lot of general reasons. This is mostly common in large-scale, complex, or distributed applications.

These bugs are the biggest headaches for the QA teams and developers. Since they both have to work in sync most times with the adaption of agile workflows in modern SDLC, automation tools have already been widely adopted for such purposes both by the QA teams and development teams.

The need for faster software release cycles and better software quality has pushed the industry towards automated debugging. With the rise of AI/ML, or other methods and techniques, it is on the brink of becoming the next big thing and the future seems bright for the same. Although there might be a steeper learning curve considering the complexities of automation itself, the efficiency that it provides outweighs the difficulties in using it.

BetterBugs was developed with similar principles. It aims to simplify bug reporting and debugging by acting as an automation layer to bridge the gaps in bug management making it even faster and more efficient.

This post will provide a holistic view of the subject of automated debugging and also include many aspects of the subject which served as an inspiration for our team to create the tool.

Let’s begin.

What is Automated Debugging?

Before jumping into the topic of automated debugging let’s try to understand the fundamental aim of automated debugging in simple terms.

When a program fails to run in the expected way, the programmers try to find the issues and solve the root cause of the problems. This is generally done in two important steps. First, the issue has to be reproduced to pinpoint the problem areas. The second step is to find out why the issue happened in the first place or the root cause.

These steps are very time-consuming, tedious, and laborious for both the QA engineers and the developers. Automated debugging aims to find the causes of the issues easily and efficiently.

Automated debugging is the process of using automated tools or relevant techniques to locate or detect issues, understand or find the root cause, and then fix those issues. It can be performed in every stage of the development lifecycle.

What are the Approaches to Automated Debugging?

Below are some of the approaches that can be followed while considering automation as the primary objective of debugging:

Fault Localization

This approach aims to find the exact location or pinpoint the place that is the root cause of the issue. The time-consuming and tedious nature of this approach is one of the main reasons for the need for automated debugging techniques for this one. It’s not an easy task either. When done manually, this technique requires a deep understanding of the program at hand. The programmers also need to have good experience in debugging for this. Additionally, the expertise to detect the cause depends upon logical thinking, the possibility for the bug location narrowed down to the suspected location, and validation if the error is coming from the same module or code being looked at.

Program Slicing

A program usually is a combination of many functions and parts. Program slicing aims to find the cause of the fault or an issue by narrowing down the parts that could have been the real cause of the issue.

To make it simpler, let’s consider a program that does addition, subtraction, division, and average operations. Now it's very much possible that the average provided by a program is wrong due to a fault in the addition operation. Thus, instead of checking the average operation for it, the addition operation has to be corrected which is the root cause of the wrong output for the calculated average.

Program slicing is generally put into the analysis domain. It is subdivided into two major types:

Static Analysis: In this type, the program is analyzed in the static state or when it's not being executed. It tries to find out the cause of the potential bugs and errors or violations in the programming standards resulting in an issue
Dynamic Analysis: Like the name, it focuses on finding the causes of errors while the program is running or being executed. It also analyzes and monitors how the program performs and responds while interacting with other modules or components within the system.

Delta Debugging

In this approach, the test case that is being considered is broken down into the smallest parts to detect, and reproduce the main cause of the issue and then fix it.

For example, consider that we have a text file containing the test case with all the input values. The text is broken down into two parts A and B. These two parts are tested.

Part A as an input causes no error, the part B input throws an error.
Now part B is again divided into B1 and B2. The B1 part input throws an error and B2 passes.
Again B1 input is divided into B11 and B12. B11 input produces no error and B12 input throws error.
If B12 input is the smallest part of the input, it is the root cause of the error.

The idea is to automate debugging with the above approach in which all parts of the test input are significant to reproduce the bug breaking it down into minimal test cases to find the cause accurately.

Execution Synthesis

Reproducing bugs or issues is one of the fundamental steps to finding the cause. Bug reports play an important part in this. It only makes sense to have the maximum information to reproduce the issue for debugging. Execution synthesis is the right term for it. When a bug is reported by the QA engineers, the developers need to know how the program got to the point that it throws an error while being run.

Execution synthesis consists of two parts:

Static Analysis (like we mentioned before): This helps the developers analyze the code with the available bug report and scan through the program to see the scope of the error. In simple terms, the program is analyzed to see what the code can and cannot do.
Symbolic execution: With the detailed bug report, the developer tries to execute the program with some unknown values to see the results.

These two methods when combined help to find the cause of the errors. Automation of these steps with the help of proper bug reports can make the process more efficient and less tiring.

Source: Automatic Debugging Approaches: A Literature Review

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What are the Benefits of Automated Debugging?

Automated debugging offers several benefits. Despite the challenges associated with it (we’ll discuss this further in the post), it can add huge value to developing quality software. Below are some of them:

Saves time and reduces costs

Manual debugging takes time. Although the benefits for the same cannot be denied, when paired with automated debugging, it immensely boosts testing and development workflows. This can save time for repetitive and even many complex tasks that would be a pain while testing and debugging manually. Eventually, it helps in optimizing and reducing costs. In a nutshell, it improves the overall productivity of the SDLC.

Better resource allocation

There are cases where manual debugging in the SDLC is the only option. With automated debugging saving time and costs, the resources for manual debugging can be allocated judiciously. As a result, it improves the quality of the software in a much better way enabling comprehensive debugging efficiently.

Improved debugging quality

Automation makes sure that nothing is left overlooked regardless of the complexity of the program. This helps in detecting the issues and resolving them without any oversight. The results can be monitored through pre-defined KPIs to keep a check on consistency.

Skill development in debugging

Automated debugging requires a good amount of programming and logical skills. Although automated debugging may not be very widely used there is no doubt that it is going to be an important component in the days to come. The steeper learning curve to handle the tools in a better way would require fair experience with programming. The debugging skills are enhanced by the same to gain knowledge and skills as QA engineers and software developers.

What are the Challenges in Automated Debugging?

Automated debugging seems hard to resist but they present a set of challenges. Although automation is inevitable in the near future for most software products, these challenges are being worked upon, especially with the integration of AI/ML and similar other technologies in the tools that help perform automated debugging.

Let’s go through some common challenges associated with it:

Measurable effectiveness

Automated debugging is still a bit foggy when it comes to debugging with assumptions. It also impacts the accuracy of results in complex scenarios that are difficult to simulate with automated debugging tools. For example, the bug detection rate, the bug fix quality, the false positives rate, etc. are still not as accurate or effective as expected.

Integrating with the existing tools

Configuring automated debugging for several testing and development tools is also not that easy. With the emergence of so many frameworks for different teams in the SDLC, automated debugging can be a challenge since it requires really good programming skills and a deeper understanding of debugging to make it work throughout.

Maintenance issues

Automated debugging tools are usually not standalone applications. They work with multiple dependencies and integrated third-party libraries. This makes them difficult to maintain once integrated with the existing system. If there are some breaking changes in their dependencies, it might very well affect the automated tool that uses it.

Increased complexity

Modern software products are complex. The increased functionality, scalability, diversity of users, and similar factors present roadblocks for automated debugging tools to work with all possible and unpredictable scenarios.

Dynamic workflows

The wide adoption of agile development methods to develop software products has led to frequent requirement modifications and scope changes. In light of these, the QA engineers and the software developers can be caught in between to consider all scenarios with automated debugging techniques. This poses difficulties in implementing such tools in the fast-paced development cycles aiming to meet deadlines and dynamic updates.

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The Future of Automated Debugging

The challenges and roadblocks for automated debugging are still concerning but that does not mean to derail it from being unified in the current and upcoming technologies.

It has a lot of potential to excel and it promises to present exciting areas for research and development to be a part of the technology trends.

Below are some of the domains that augment automated debugging to become better for today and the future:

Artificial Intelligence/ Machine Learning

AI/ML enables automated debugging tools to train on the existing data. Big corporations and even many similarly visioned companies seem unstoppable in utilizing this data with all the computing power and resources that they have. Automated debugging tools benefit from it and will keep on improving with the current AI/ML trend.

Big Data

With the abundance of data especially with Big Data in play, there is no better way to keep on with quality software in the absence of automated tools. Automated debugging can become more accurate and reliable in handling complex data and scenarios augmented with Big Data for training better. This would enable automated debugging tools to improve the fault tolerance and adapt to the dynamic needs.

LLMs

Large language models or LLMs are based on neural networks utilized in deep learning techniques. They have the ability to process a given prompt or input statement to generate natural language text. Technically, they are trained on massive datasets for a better understanding of the prompt statements or the provided inputs. They can be used for many tasks like code repair, code analysis, and finding issues in a program. LLMs have a great potential for automated debugging and can provide valuable insights through efficient analysis. However, it's important to be careful of the results.

Wrapping Up

It won’t be wrong to say that automated debugging still has to address many shortcomings. But let’s face it. It’s already here and being used in many ways in the software industry.

Automation debugging cannot be ignored for too long with so many other technologies fueling its betterment and relevancy.

BetterBugs addresses many shortcomings of the current gaps in using automated debugging tools in a very intuitive way. Its collaborative and easy integration capabilities with simple and thought-out user flows aim to improve productivity across teams.

Our team sincerely hopes that you enjoyed reading the post.

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