Leveraging Automation To Enable Incremental Testing

The Problem of Regression Testing

Regression testing refers to the practice of re-running functional and non-functional test suites every time an application change is made to ensure that existing functionality has not been unintentionally impacted. For large and complex applications with extensive test suites, the cost of re-running full regression test suites starts to hinder release velocity and becomes prohibitive.

As engineering teams aim to increase deployment frequencies to enable faster delivery of features and defect fixes to customers, the lengthy regression testing cycles severely throttle the ability to release changes frequently. Teams either end up not running comprehensive regression testing, thereby compromising quality, or accumulate significant technical debt in the form of deployment queue backlogs.

The fundamental conflict exists between the business need to accelerate feature delivery to delight customers with the quality mandate of thoroughly testing application changes to prevent defects from impacting those same customers. The traditional practice of full regression suites with every change is no longer practical or scalable.

Introducing Incremental Testing

Incremental testing refers to the selective re-execution of only those tests deemed necessary to validate code changes, as opposed to re-running all tests in discrimatory fashion. The core principles of incremental testing are:

• Analyze code changes to identify artifacts impacted
• Map impacted artifacts to relevant portions of test suite
• Run only those subset of tests touching impacted areas

Instead of treating all changes equally and mandating full retest, incremental testing aims to restrict testing to areas directly exercising the code modifications. This focused approach can significantly accelerate validation cycles while still providing meaningful regression risk coverage.

Automating Impact Analysis

The keystone of efficiently implementing incremental testing lies in programmatically identifying the downstream impact of code changes prior to test execution. Static and dynamic analysis techniques can automatically trace code changes to downstream dependencies:

• Static analysis examines source code structure without execution to identify dependencies
• Dynamic analysis profiles running applications to map actual dependencies

By leveraging these techniques, test selection logic can reliably determine the portions of the code base and tests that could be potentially affected by proposed changes prior to deployment. Tests exercising functionality with no traced dependency on changed code can then be safely eliminated from rerun while still providing comprehensive validation coverage where needed.

Implementing Automated Test Selection

To realize the benefits of incremental testing, the capability to automatically select subsets of tests based on change analysis should be embedded within the continuous integration / continuous delivery (CI/CD) pipelines.

Test selection rules can be configured through declarative policy to customize inclusion/exclusion criteria. For example, criteria could specify minimum dependency trace distances for tests to be selected or mix dynamic and static analysis with risk-based probabilities.

Integrating this directly into existing automation workflows allows accelerated validation cycles to occur on every code commit without disruption. Making automated analysis and selection decisions eliminates the need for human judgment while still providing tunable guardrails.

Maximizing Test Parallelization

The use of test parallelization techniques complements incremental testing by further reducing the wall clock time needed to complete subset test executions resulting from change analysis. Tests can be automatically partitioned across multiple virtual machines to maximize resource utilization.

However, care must be taken to avoid any duplicate tests across the parallel runs which could lead to overestimating coverage. The test distribution logic should account for dependencies between test cases to guarantee each item is run only once across the collective parallel executions.

Harnessing scalable, on-demand infrastructure provides capacity to spread the execution work and minimize redundancy. Combining with incremental test selection provides exponential efficiency gains in validation cycle times.

Measuring Effectiveness

To quantify the actual efficiency improvement realized via incremental testing automation, both coverage relative to full suite runs along with comparative defect finding rates should be measured.

Coverage can be evaluated through tracing of test cases back to product code exercised with correlation to coverage by full test executions. Any significant deviation would indicate gaps introduced by incremental runs.

Defect finding ability should also be compared between incremental and full runs against release changes over multiple iterations. Variability in defect detection between the two would pinpoint any deficiencies with test selection and scope.

Longitudinal metrics in both coverage and quality measures validate ROI and guard against incremental testing divergence from full suites.

Overcoming Adoption Challenges

Despite the sizable benefits, adopting incremental testing necessitates executive buy-in along with architectural & cultural shifts. The value proposition should be framed around tangibly aligning testing with business velocity objectives while preventing quality erosion.

Technical hurdles such as tracing support for dependency analysis or test virtualization can follow iterative rollouts. Starting with risks or releases where quality differential matters less eases adoption.

With demonstrable ROI, incremental testing ripe for extensive leverage across testing scenarios requiring responsiveness without compromising confidence.