Exploratory testing is a foundational software testing approach where testers actively learn about the system under test and design tests concurrently while executing them. Unlike scripted testing with predefined test cases, exploratory testing relies on the tester’s skills, intuition and judgment to dynamically explore the software’s behavior. However, as systems grow vastly complex, purely manual exploration strains to achieve sufficient test coverage.
This is where large language models come in – AI systems trained on massive text data that can understand and generate natural language. Prominent examples include ChatGPT, Google’s LaMDA and Anthropic’s Claude. These models’ language mastery enables a revolutionary expansion of exploratory testing through conversational interfaces. By automating laborious testing tasks while amplifying human capabilities, large language models may unlock transformative gains in efficiency, coverage and software reliability. The combination of human creativity and machine scale promises to redefine exploratory testing economics. Testing cycles can accelerate exponentially through AI test case generation and parallel test execution. Human testers gain higher-level time to focus on judgement-intensive activities. AI exploration provides comprehensive coverage exceeding manual means. Democratization and access also increase via conversational interfaces.
The ascent of AI marks exploratory testing’s evolution from purely manual to increasingly machine augmented. Testing stands on the cusp of a new era offering boundless potential benefits if harnessed judiciously. This article will dive deeper into this transformative intersection of exploratory testing and artificial intelligence.
The Power of Conversation: Automating Exploratory Testing Workflows
A primary advantage of large language models is their ability to understand questions and requests posed in everyday language. Testers can leverage conversational interfaces to automate test design, execution, data generation, and reporting.
For example, instead of manually creating test cases and datasets, a tester could describe desired test scenarios and data parameters conversationally. The AI assistant can rapidly generate robust test cases and datasets tailored to those specifications. This exponentially amplifies the tester’s productivity.
By delegating lower-level testing tasks to the AI, testers regain time and mental energy. This empowers them to pursue deeper domain exploration through higher value testing activities. Human judgment remains essential for assessing subtleties, anomalies and evaluating test results. AI augmentation enhances human capabilities rather than replacing them outright.
Accelerating Testing Cycles Through Parallelization
Large language models also enable concurrent, parallel test execution at scale. A tester may spend hours or days running manual tests in a linear sequence. In contrast, an AI assistant can simultaneously run thousands of tests in parallel against software builds and environments.
By drastically compressing testing cycles, defects surface earlier when fixes take less time and cost. These complements accelerated development workflows like continuous integration and delivery. Testing velocity and agility increases markedly.
One analogy is leveraging AI testing assistants as a “clone army” that can fan out to cover far more test ground than any manual approach. This surrounds the software with an encompassing safety net to catch defects before they reach customers.
Expanding Test Coverage Through AI-Driven Exploration
Pure manual testing often relies on testers cashing in on domain knowledge to explore high-risk areas. However, testers cannot foresee every scenario – especially for complex, rapidly evolving systems. Gaps in test coverage can lead to unpleasant surprises down the line.
Here, AI exploration complements human domain expertise. For example, testers can prime AI assistants with initial interfaces, workflows, and data models. The AI will then intelligently expand test coverage by automatically generating additional tests to traverse unexplored usage paths.
Essentially, the AI fuzzes and breaks the system in ways testers may not have conceived. The net result is more rigorous test coverage and exposure of subtle defects. AI becomes a partner multiplying the tester’s reach and impact.
Broadening Access and Democratization
Since AI testing assistants utilize conversational interfaces, they also lower barriers to effective testing for those lacking specialized skills. Business analysts, developers, and other stakeholders can contribute to testing initiatives earlier in the lifecycle without formal training.
This democratization gives non-testers a simple way to validate requirements and interfaces. It also fosters a culture of collective responsibility for quality rather than siloing testing work. The outcome is fewer hand-offs, quicker feedback loops, and reduced risk.
Judgment and Oversight: The Vital Human Role
While AI amplifies exploratory testing capabilities, human judgment remains essential:
- Assessing subtle differences between expected and actual test results requires discernment – is a slight UI layout variation a true defect?
- Ethically testing medical software necessitates human oversight so patient safety stays paramount. AI has no innate concept of ethics.
- Evaluating not just whether tests pass/fail, but interpretting the wider implications and severity requires human reasoning.
- Governing how extensively to deploy AI testing assets given budget and resource constraints is a nuanced, context-specific decision.
In short, humans must oversee when and where to deploy AI testing assistants. Software quality hinges on strategically combining the strengths of man and machine.
Transparency and Bias: The Need for AI Audits
However, expanded use of AI also raises valid concerns around transparency and bias. Unlike rules-based algorithms, the inner workings of large language models remain opaque.
Undetected biases and irregularities within training data can lead to unreliable testing processes. For example, imperfect training could result in overlooking defects or valuable test cases.
Addressing this requires extensive audits. Testing teams should conduct sample-based reviews of machine-generated test cases and results. AI training and inference processes should be independently validated. Such rigorous scrutiny ensures AI reliability and builds stakeholder trust.
Implications for Development and Deployment
The rise of AI test assistants stands to profoundly impact software development, deployment, monitoring and maintenance:
- By front-loading testing earlier in development cycles defects become cheaper to fix. This will accelerate the shift towards agile, DevOps and CI/CD workflows.
- Automated test case generation and parallel test execution will allow real-time integration of testing into the deployment pipeline.
- AI exploration may surface unknown defects in production software, catalyzing corrective updates and hot patches to mitigate threats.
- Continuously testing production systems will enable preemptive maintenance, saving costs and preventing customer-impact failures.
In essence, AI testing automation will allow quality assurance to scale alongside the rapid speed, size, and complexity of modern software.
The Future of Exploratory Testing
Exploratory testing is undergoing a renaissance. AI augmentation provides a compelling way to close testing gaps, accelerate cycles, and keep pace with continuous delivery.
However, a testing transformation requires judiciously integrating AI tools into workflows while preserving human oversight and validation. To realize the full benefits, organizations will have to actively reimagine processes, culture, and team structures.
With prudent adoption, large language models can redefine exploratory testing economics and effectiveness – ushering in an era of safer, higher-quality software development. The possibilities are transformative. But as with any powerful technology, we must wield AI testing assistants thoughtfully, transparently, and ethically for maximum benefit.
By Arvind Kumar Bhardwaj, IEEE Senior Member at Capgemini
Arvind Kumar Bhardwaj is currently working in Capgemini. He is a Technology Transformation Leader with 18+ years of industry experience in Business Transformation, Software Engineering Development, Quality Engineering, Engagement Management, Project Management, Program Management, Consulting & Presales. Arvind is a seasoned leader with experience in managing large teams, successfully led onshore and offshore teams for complex projects involving DevOps, Chaos Engineering, Site Reliability Engineering, Artificial Intelligence, Machine Learning, Cyber Security, Application security and Cloud Native Apps Development.
Arvind is IEEE Senior member, Author of the book “Performance Engineering Playbook: from Protocol to SRE” and co-Author of book “The MIS Handbook: Strategies and Techniques”. He is an “Advisory Committee” Member, 9th International Conference ERCICA 2024 and IEEE OES Diversity, Equity, and Inclusion(DEI) Committee member. Arvind holds 2 Master degrees in computers and business administration. Arvind has published research papers in major research publications and technical articles on dzone.com and other major media. Arvind served as a industry expert and judge for reputable award organizations in Technology and Business which include Globee Awards, Brandon Hall Group, Stevie Awards, QS Reimagine Education Awards and The NCWIT Aspirations in Computing (AiC) High School Award. Arvind is a senior coach and approved mentor listed in ADPlist organization.