Maneesh Sharma is the COO of LambdaTest, an AI-powered unified enterprise test execution cloud platform. Sharma emphasizes the importance of constant evolution for tech companies amidst ongoing challenges, a competitive landscape, and rising customer expectations. As applications and systems grow in complexity, organizations need to ensure everything is performing optimally. Observability plays a crucial role in providing insights into app or web behavior and performance, aiding in bug identification and resolution.
Implementing observability into testing can offer 360-degree visibility into the tech stack, helping to avoid future failures or performance bottlenecks. Detailed performance insights, improved customer experience, and slashed bug fixes are some of the key benefits of incorporating test observability. Observability aligns with key telemetry types and DevOps tools, leading to a unified perspective on application performance, proactive issue resolution, effective performance management, and delivery of high-quality software products.
Incorporating observability into software testing requires a structured approach, starting with defining specific testing objectives and instrumenting code with meaningful logs. Identifying critical key performance indicators and choosing the right tools for logging and tracing are essential steps. Setting up reporting dashboards for visualization helps in monitoring the testing process and provides valuable insights for ongoing improvements. However, challenges such as integration and compatibility issues, data overload, skill gaps and training, budget constraints, and cultural shifts and security balance need to be addressed.
AI and ML are increasingly being used to enhance the test observability process in the software development industry. Predictive insights, automatic data analysis, improved fault detection, and faster problem resolution are some of the ways AI/ML can contribute to test observability. Companies must set clear goals and expectations when adopting AI/ML, focusing on specific tasks such as detecting data drift or identifying anomalies. Ensuring high-quality, well-labeled data for training prevents unreliable AI/ML outputs.
The future of test observability looks promising, with IT and DevOps teams facing pressure to resolve issues swiftly, understand root causes, and implement measures efficiently. Test observability is expected to play a crucial role in reducing downtime and enhancing system reliability in software development by 2024 and beyond. Sharma predicts that observability will help meet rising customer demands and achieve operational efficiency in the evolving technological landscape.
