- SolutionsSoftware testing
- TechnologiesDeepTriage, ChatGPT, Grammarly, Microsoft Power BI
- Country Finland
At QArea, we pride ourselves on delivering superior software testing services to our clients. One of our valued clients stands at the forefront of creative innovation with their custom consoles, marrying analog control and digital precision. These consoles empower creators with the speed and fine-tuning capabilities they need to bring any vision to life confidently.
Beneath the prowess of their products, our clients grappled with a series of challenges within their testing process. These complexities spanned across bug triage, test coverage, bug report creation, requirements testing, and report creation.
Recognizing the intricacy of these issues, we, at QArea, saw an opportunity not just to resolve their immediate challenges but to elevate their operational efficiency. Leveraging the transformative power of AI tools, we aimed to streamline the processes, enhancing their overall efficiency, and ensuring they continue to deliver the high-quality, precision-oriented products they are renowned for.
Senior QA — 1
In the process of revolutionizing software testing through AI, we encountered a series of testing roadblocks that demanded innovative solutions. These ranged from dealing with duplicated bug issues to enhancing test coverage and refining bug report creation, all the way to reimagining requirement testing and efficient report generation.
Overcoming Key Testing Roadblocks
Bug triage. Duplicated issues were a recurring problem, resulting from multiple authors logging defects into the bug tracking system.
Test coverage. Due to high-priority test execution activities, time for comprehensive documentation creation was often compromised, predominantly covering only positive scenarios.
Bug reports creation. The issues our client faced were being discussed across various forums and thematic groups, yet the simplicity of the textual explanations from customers necessitated a conversion into a more formal bug format.
Requirements testing. There was a need for structured user stories, conforming to a common user story creation approach.
Report creation. Report creation during regression testing linked data from disparate resources, a process that was significantly time-consuming.
Facing the Challenges of AI Integration in Testing
While tackling the current software testing processes, we’ve encountered additional challenges that extended beyond their initial concerns.
Confidentiality and data security. Given the sensitive nature of the software we were working with and the proprietary technology involved, maintaining the confidentiality of their data and ensuring the security of their software was paramount. Strict protocols had to be enforced during the testing process to prevent data leaks or any potential breaches.
False positives and negatives. When utilizing AI tools for software testing, there’s always a risk of encountering false positives—issues flagged as errors that are actually correct—and false negatives—actual errors that are overlooked. Mitigating these false responses, which could lead to wasted resources or overlooked problems, was a significant challenge.
Tool integration and compatibility. Another challenge was to integrate and make compatible the different AI tools—DeepTriage, ChatGPT, Grammarly, and Microsoft Power BI—with our client’s existing system. A smooth and seamless integration was crucial for the efficient operation of the AI tools and to prevent any disruptions to the current workflows.
Continuous monitoring and adjustment. AI tools, while highly effective, need continuous monitoring and adjustments to provide optimal results. The challenge was to ensure consistent and regular oversight, tweaking the AI models when needed and ensuring they evolve with the changing software dynamics and requirements.
Addressing these additional challenges required careful planning, strategic decision-making, and effective communication with our client to ensure a successful overhaul of their software testing process.
To navigate the mentioned challenges, QArea suggested the adoption of several AI tools:
Bug triage. We recommended the use of DeepTriage, an AI-powered tool developed by Microsoft researchers. This tool leverages machine learning to streamline the bug triage process and assign bugs efficiently.
Test coverage. We deployed ChatGPT to work with the text format, typically represented by requirements, and retrieve test cases from it.
Bug reports creation. We utilized ChatGPT to analyze, structure, and rewrite customer experience reviews into a format suitable for enhancement and issue reports.
Requirements testing. ChatGPT and Grammarly were jointly used to restructure, analyze, and improve software requirements, ensuring a consistent style and correcting spelling errors.
Report creation. For creating cohesive and efficient reports, we utilized Microsoft Power BI. This tool supports data integration from various sources, offers data modeling capabilities, and provides AI-driven insights.
Practical Approaches: Enhancing Software Testing with Focused Strategies
Our team implemented several strategic solutions to ensure a seamless transition and an efficient software testing process.
To safeguard data confidentiality, we enforced security protocols and implemented encryption techniques. Also, we have never presented AI tools with 100% true data, changing numbers, names and places, as well as any information that may have been considered sensitive. Later, we have used an algorithm to use the true data at our side and receive precise results.
To mitigate the issue of false positives and negatives, we employed validation techniques and cross-checks in our testing process. This approach ensured that any errors flagged by AI tools were verified, and any overlooked errors were caught in subsequent testing stages.
For the smooth integration of AI tools, we performed careful compatibility testing and gradually introduced the tools, ensuring there were no disruptions to existing workflows. Additionally, we provided technical support during and after the integration process to address any emerging issues promptly.
We organized a few training sessions for the client’s team to understand and effectively use the new AI tools. Additionally, we provided detailed documentation and ongoing support to help the team adapt to the new tools and processes.
Finally, as requested by our client, we established a dedicated team to monitor the performance of the AI tools continuously, adjusting parameters and refining the AI models as needed. By maintaining a close watch on the tools’ performance and making timely adjustments, we ensured that they continued to deliver optimal results, keeping pace with the evolving dynamics and requirements of the software.
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The time for analysis and bug report creation decreased by 80%. Defectability was also dramatically reduced by 90%.
The process of creating a test case became 80% faster. The coverage of edge cases increased by 40%, and defectability saw a reduction of 90%.
The requirement testing time was cut by a staggering 500%. Spelling mistake corrections saw an increase of 50%, and overall accuracy improved by 10%.
Data representation saw a 30% improvement, and the time to create a report was halved.
This case study clearly demonstrates how the strategic deployment of AI tools, coupled with QArea expertise, can dramatically enhance the efficiency and effectiveness of software testing processes, providing significant value to our clients.
Conversion of mobile app
Bounce rate decrease
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