Guide

What Is Exploratory Testing? How It Works and How to Automate It in 2026

Exploratory testing is simultaneous test design, execution, and learning. How it works, when it beats scripted testing, and how AI agents now automate it.

Dhaval Shreyas

CEO & Co-founder

10 min read Posted Jun 9, 2026

What Is Exploratory Testing?

Exploratory testing is an approach to software testing in which a tester simultaneously designs tests, executes them, and learns from the results, all at once, with no pre-written script. Instead of following predefined steps, the tester explores the application, forms hypotheses about where it might fail, and uses each result to decide what to investigate next.

The term was coined by Cem Kaner in his 1984 book Testing Computer Software. Kaner later defined it as “a style of software testing that emphasizes the personal freedom and responsibility of the individual tester to continually optimize the quality of his or her work by treating test-related learning, test design, test execution, and test result interpretation as mutually supportive activities that run in parallel throughout the project” (see kaner.com). The ISTQB glossary classifies it as an experience-based technique, distinct from scripted approaches where test cases are designed in advance.

The key word is simultaneous. In scripted testing, design happens weeks before execution. In exploratory testing, the gap collapses to zero. You learn the product and test it in the same motion, which is exactly how its sharpest defects get found.

Exploratory Testing vs. Scripted Testing

The difference between exploratory and scripted testing is when test design happens. Scripted testing designs every step in advance, then executes it mechanically, which is ideal for repeatable regression checks. Exploratory testing designs as it goes, adapting to what the application reveals, which makes it ideal for discovering defects no one anticipated. They are complementary, not competing.

A 2007 study by Juha Itkonen and Kristian Rautiainen, “Defect Detection Efficiency: Test Case Based vs. Exploratory Testing,” found that exploratory testing detected a comparable number of defects to scripted, test-case-based testing, but without the upfront cost of designing and documenting those test cases. The exploration carried its own efficiency: less ceremony, similar yield. That result is why mature teams stopped treating exploratory testing as “just clicking around” and started treating it as a discipline.

The two approaches answer different questions, which is why most teams run both:

Scripted testing protects what you know works. Exploratory testing investigates what you haven’t checked yet. A team that only scripts ships products that pass every test and still surprise users in production.

How Exploratory Testing Actually Works

Exploratory testing works through structured improvisation: a tester picks a focus area, explores it within a fixed time window, and follows discoveries wherever they lead. The structure that makes it accountable, rather than aimless clicking, is Session-Based Test Management (SBTM), introduced by James and Jonathan Bach in 2000 (documented at satisfice.com).

SBTM organizes exploration into three repeatable parts:

• The charter. A short mission statement for the session, stating what to explore and why. For example: “Explore the checkout flow with expired payment methods to discover error-handling failures.” The charter gives direction without dictating steps.
• The session. A time-boxed block, typically 60 to 120 minutes of uninterrupted testing against the charter. The tester takes notes on what they tried, what they found, and what new questions emerged. Michael Bolton, who co-developed Rapid Software Testing with James Bach, stresses that note-taking is what separates skilled exploration from random poking.
• The debrief. A short review after the session where the tester reports coverage, defects, and follow-up charters. This is where exploratory testing becomes manageable: leads can see what ground was covered without prescribing every click.

The loop repeats. Each debrief spawns new charters, each session deepens the team’s model of the product, and coverage compounds. Crucially, the tester’s learning is the engine. Exploratory testing gets better the more the tester understands the system, which is why domain knowledge matters more here than in scripted work.

When to Use Exploratory Testing (and When Not To)

Use exploratory testing whenever uncertainty is high and pre-written scripts would be guessing. It excels at brand-new features with no established behavior, complex multi-step user journeys, recently fixed bugs that deserve a probing re-check, and usability or experience problems that automated assertions never evaluate. Anywhere the question is “what else could go wrong here?”, exploration is the right tool.

It is a weaker fit for a few situations, and pretending otherwise is how teams misuse it:

• Pure regression at scale. Re-verifying hundreds of known behaviors every release is repetitive, deterministic work. Automate it. Exploration on stable, unchanged features wastes a skilled tester’s judgment.
• Strict compliance evidence. When an auditor needs proof that an exact, prescribed sequence was executed, scripted test cases with step-level records fit better than session charters.
• Load and performance measurement. Throughput and latency under stress need load and performance testing tools, not human exploration.

Exploratory testing is a discovery engine, not a regression safety net. Teams that try to make it carry both jobs end up doing neither well. As the ISTQB materials note, experience-based techniques are designed to complement systematic ones. Pair exploration with a solid regression test suite and each covers the other’s blind spot.

Exploratory Testing Techniques That Find Real Bugs

Exploratory testing has concrete techniques that turn open-ended curiosity into reliable defect-finding. The most influential is the tours metaphor from James Whittaker’s book Exploring Software: the idea that a tester can “tour” an application the way a visitor tours a city, each tour with a different theme that surfaces a different class of bug.

A few tours that consistently earn their keep:

• The Money Tour follows the features that generate revenue, like sign-up, checkout, and subscription upgrades, because that’s where failures cost the most. Pie’s own emphasis on prioritizing high-risk, revenue-tied flows is the Money Tour applied at machine scale.
• The Landmark Tour visits the major features in a deliberate order, then reorders them, exposing state bugs that only appear when users navigate in unexpected sequences.
• The Bad Neighborhood Tour revisits the parts of the app that have produced bugs before. Defect density clusters, so where you found one, you’ll often find more.

Two heuristics make any tour sharper. SFDPOT (Structure, Function, Data, Platform, Operations, Time) from James Bach’s Heuristic Test Strategy Model is a checklist of dimensions to attack. And there’s the classic tester’s reflex: change one input to something unexpected (empty, enormous, negative, emoji, a paste of 10,000 characters) and watch what breaks. That turns a calm flow into a stress test in seconds. These aren’t random pokes; they’re patterns, and patterns are exactly what a machine can learn.

Can You Automate Exploratory Testing?

You can now automate the exploration itself, not just the replay of recorded steps. For decades, “automated exploratory testing” meant monkey testing: bots firing random taps to crash an app. That found shallow stability bugs but had no judgment, no memory, and no sense of what mattered. In 2026, AI agents changed the equation by bringing the missing ingredient, an evolving model of the application.

Modern AI testing agents navigate an app the way a human explorer would: they perceive each screen, build a map of available actions, form goals, and probe paths that look risky or under-tested. This is genuinely different from scripted automation, which can only do what it was told. An agent doing autonomous test discovery decides where to go based on what it observes, which is the defining trait of exploration.

There’s a real limit worth stating plainly: an AI agent doesn’t have a product manager’s intent or a user’s emotional context, so it won’t always know that a technically-valid screen is a bad experience. Human exploratory testing still wins on subjective judgment. But for the mechanical core of exploration, systematically covering flows, hammering edge-case inputs, and surfacing states a human would take days to reach, agents now do in minutes what used to consume entire test sessions. The right model isn’t “AI replaces the explorer.” It’s “AI does the legwork so the human explorer spends their judgment where it counts.”

How Pie Automates the Exploration Itself

Pie is built around the exact thing exploratory testing has always depended on and never been able to scale: an agent that explores your app, learns it, and decides what to test. Where a human tester runs one 90-minute session at a time, Pie’s autonomous agent explores your entire application, maps every screen and interaction path, and prioritizes the high-risk flows (auth, payments, anything tied to revenue) automatically.

This is exploration as infrastructure:

• Autonomous discovery is the Money Tour at scale. A Pie agent navigates your app without a script, finds the flows that matter, and generates a working test suite from them. For an average app the first suite lands in roughly 30 minutes with 60–80% coverage out of the box, the kind of broad reconnaissance that would take a human explorer days of sessions.
• Vision-based execution explores like a user, not a parser. Because Pie identifies elements by what’s rendered on screen rather than by brittle selectors, it can navigate UI it has never seen before, which is exactly what exploration requires. That’s also why the resulting tests don’t shatter on every redesign the way selector-based suites do.
• Natural-language charters for the flows an agent can’t infer. You describe the edge case in plain English, like “explore checkout with an expired card across regional pricing,” and Pie’s agent executes it. That’s a session charter, handed to a machine.

The payoff is concrete. Fi, the smart dog collar company that adopted Pie, moved to same-day releases because coverage stopped being the bottleneck. The agent re-explores and re-validates on every change instead of waiting for a human session to free up. Exploration that once gated the release now runs continuously alongside it.

Exploration Is a Skill. Now It Scales.

Exploratory testing has always been the part of QA that found the bugs that mattered: the ones no script anticipated, surfaced by a curious human designing and learning in real time. The discipline that Cem Kaner named and James Bach and Michael Bolton sharpened is as relevant in 2026 as it was in 1984. What’s changed is that the legwork of exploration (covering flows, mapping states, hammering edge cases) no longer has to be done one human session at a time.

The teams winning now don’t choose between human judgment and machine scale. They let AI agents do the broad, tireless exploration and reserve their testers for the subjective calls only a human can make. Scripts guard what you know. Exploration, now automated, discovers what you don’t. Run both, and production stops surprising you.

Dhaval Shreyas

CEO & Co-founder

13 years building mobile infrastructure at Square, Facebook, and Instacart. Now building the QA platform he wished existed the whole time. LinkedIn →