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Using AI to Resolve Bugs in Your Online Store

Using AI to Resolve Bugs in Your Online Store

We all know that maintaining a seamless user experience is crucial for the sales growth of your online store. However, bugs and issues are inevitable – the software development lifecycle (SDLC) is a continuous battle against them.  Bugs can still arise despite meticulous planning and product development, leading to costly delays, frustrating user experiences, etc. and execution. Here is where opportunities lie in the use of AI to resolve bugs, offering an innovative approach to detect and analyze errors in your ecommerce. 

In a previous article, we looked at how AI could be used in Quality Assurance. This time, we go deeper into the current and potential applications of AI for bug detection and analysis. We will also take a look at the benefits and challenges of using AI for this purpose.

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Benefits of Using AI to Resolve Bugs

Bug tracking and resolution are essential tasks for any ecommerce development project. However, these activities are often time-consuming, tedious, and prone to human error. AI has the potential to make the entire bug resolution process easier, helping with activities such as detection, classification, prioritization, assignment, and resolution. Let’s first take a look at some of the benefits at a high-level:

Speed and Efficiency

One of the key advantages of AI is that its algorithms can analyze vast amounts of data, identify patterns, and generate solutions at lightning speed. Imagine having the ability to pinpoint bugs and recommend solutions for your online store in a matter of seconds. AI’s rapid problem-solving capability can minimize service downtime, ensuring that your online store operates smoothly for customers at all times.

Accuracy and Consistency

AI eliminates the margin for human error, delivering accuracy and consistency in problem resolution. Unlike humans, AI algorithms are not influenced by biases or fatigue, ensuring every solution is precise and reliable.

Cost-Effectiveness

Test automation is another key benefit. AI not only enhances efficiency but also drives cost savings through its use in routine problem resolution tasks. Organizations can then reallocate resources from manual tasks to focus more on strategic initiatives. 

AI for Bug Detection

There are a number of ways where AI is used to identify bugs in the development process. AI algorithms already have the capability to comb through various aspects of your ecommerce platform, such as code, test cases, logs, and user feedback, to spot potential bugs early in the development cycle and before they impact end-users. 

Ecommerce websites are usually visually-driven applications. Hence Computer Vision (CV) algorithms can be employed to analyze an online store’s UI/UX. It can identify inconsistencies, layout issues, or visual defects that traditional testing methods may overlook.

Using AI to Resolve Bugs - Computer Vision for visual bug detection
Using AI to Resolve Bugs – Computer Vision for visual bug detection

The future of bug detection will likely involve Machine Learning (ML). By learning from historical data containing bug reports, code patterns, and test results, ML algorithms can potentially predict the likelihood of bugs existing in new code with reasonable accuracy. This enables proactive testing efforts and allows developers to address issues before they become full-blown bugs.

AI for Bug Classification & Prioritization

The next crucial step in the bug resolution process is the classification and prioritization of defects. How can AI and ML streamline this process for your online store? 

We all know that once a bug is detected, it needs classification based on type, severity, and impact. Natural language processing (NLP) and computer vision could be utilized to analyze bug descriptions or screen captures, in order to extract relevant keywords and information. For instance, NLP tools can parse bug descriptions, labeling them as feature requests, enhancements, or defects. It could even drill down to the specific type of issue, such as functionality related, performance, security, etc. Also, there will be more tools in the market that can automatically forecast the impact of the bug on the user satisfaction, retention, and revenue.

When it comes to bug resolution, the prioritization process is essential for efficient resource allocation. AI can be fed with information on bug history, patterns, developer skills, in order to estimate the cost and resolution time of a bug. A specific example is the use of AI-powered data mining tools to identify the most frequently reported, duplicated, or reopened bugs and subsequently classify them higher in the priority list.

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AI for Bug Fixing

Once a bug is identified and classified, the final step is resolving it promptly and effectively. This involves an initial triage, identifying the root cause, testing the solution, and deploying the changes. Here are some practical applications of AI in the bug fixing process:

Automated debugging

AI-powered debugging tools can analyze code to pinpoint the root cause of errors swiftly. Such tools can suggest potential fixes and generate patches to resolve bugs efficiently. 

Code documentation

Clear and descriptive comments aid in conveying the purpose and functionality of code segments, facilitating bug resolution and code maintenance. For this purpose, Generative AI can assist in code documentation. Developers can prompt AI to add comments to code, enhancing code readability and understanding. 

Iterative bug fixing

Iterative bug fixing involves refining solutions through iterative feedback loops. Leveraging AI, developers can refine bug fixes by feeding bug descriptions and code snippets to AI models like ChatGPT. 

Challenges of Using AI to Resolve Bugs

There are 3 main challenges when it comes to the use of AI to resolve bugs, whether for an ecommerce store or for other types of software applications.

Data quality

The effectiveness of a tool that uses AI for problem resolution depends heavily on the quality of the data it operates on. Organizations must ensure that the data used to train AI algorithms is clean, accurate, and relevant. Poor-quality data only undermines the benefits of using AI.

Bias

A key challenge in AI is that its algorithms and models are often susceptible to bias. This occurs if it is trained on biased data. This leads to inaccurate results and there are no simple answers when it comes to addressing biases in AI.

Integration

Integrating AI-powered problem resolution into existing systems and workflows presents challenges. Organizations have to ensure that such AI tools are compatible with their existing IT infrastructure. Also, they have to ensure that sufficient training is provided to employees on the use and maintenance of these AI tools.

Wrapping Things Up

AI-powered problem resolution has immense promise for optimizing online store performance and enhancing user experience. However, there are a number of challenges in AI that QA and developers need to understand in order to harness its full potential in the bug resolution process. As AI continues to evolve, we can expect to see even more innovative solutions emerge, driving continuous improvement and innovation in online retail.

If you’re looking to optimize your ecommerce store and resolve bugs faster, consider using Valido AI. With its AI-powered features in bug detection and resolution, you are better equipped to boost conversions and the user experience of your online store.

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