Faster Build & Test Loops, Better DevX

I was listening to a podcast recently about building agents, and they mentioned that writing code was never the bottleneck; it was everything around code writing that took the most time. So true, I thought. Of course, there’s much more to delivering software to customers, but I’ve always cringed at how much inefficiency exists in a typical build and test setup, and how little effort teams often put into addressing these bottlenecks.

Recently, while testing a new build tool, I noticed that when it worked, it improved my build efficiency by more than 50%. That’s when it struck me how much productivity I had been losing and even more surprisingly, how unaware I’d been of it, simply because I had grown accustomed to those inefficiencies.

For projects with slow build and test setups, this often leads to developer frustration, wasted compute, and delayed releases. When the process runs efficiently, it creates a multiplier effect, where saving just a few minutes per build can translate into massive productivity gains across thousands of executions.

After all, a faster feedback cycle on new changes is one of the greatest enablers of innovation.

In this article, I’ll walk you through the typical bottlenecks and suggest some ideas on how to remove those inefficiencies.

Understanding the Bottlenecks

Typically, the bottlenecks come from the following sources:

Large monorepos and complex dependency graphs:

Monorepos often evolve into environments with complex and interconnected dependency networks, where even a small change in one module can trigger rebuilds across a significant portion of the repository. Unless there has been significant investment in dependency tracking and incremental build capabilities, typical builds take a long time to complete.

Inefficient caching:

Caching is fundamental to accelerating both builds and tests. To make it truly effective, these processes must be deterministic, hermetic, and content-addressable. Beyond caching compiled artifacts, test results should also be cached when reproducibility is guaranteed using stable inputs such as source files, dependencies, environment variables, and toolchain versions to generate consistent cache keys.

Lack of Incremental Builds and Intelligent Test Selection:

In large codebases that lack efficient build and test mechanisms, even small changes can trigger complete rebuilds and full test runs. When this happens repeatedly across commits and throughout the development lifecycle, from code creation to merging to trunk, it leads to significant inefficiency. The impact isn’t limited to longer wait times for developers; it also results in substantial compute waste from unnecessary rebuilds and test executions that add little to no value.

Overlapping or redundant tests:

Over time, test suites often become redundant as multiple tests across different layers end up validating the same logic. While this is usually well-intentioned, it can significantly increase test execution time without meaningfully improving safety, reliability, or coverage.

Insufficient infrastructure to support faster execution:

Infrastructure constraints can become a major limiting factor for speed and scalability. Compute capacity, storage bandwidth, or scheduling efficiency fail to keep pace with developer demand, throughput suffers regardless of how well-engineered the build system is. Slow or under-provisioned runners, shared resource contention, and limited parallelism often go unnoticed until queues start to build up or feedback cycles slow to a crawl.

Measure Before You Optimize

I’ll be walking through some suggestions on how to address the above bottlenecks in the next few sections, but before we get there, I want to emphasize the importance of measurement.
 Improving these bottlenecks is important, but we shouldn’t be vague about the results. We need to measure both the specific improvements achieved and the overall trends in build and test performance to truly understand our progress.

Some of the important things to measure include:

Build Time Distribution:

Track p50, p90, and p99 durations to understand typical and worst-case performance.

Test Execution Time:

 Measure both overall and per-suite execution times continuously, alongside the number of tests executed, to monitor performance trends and detect regressions over time.

Incremental vs. Clean Build Ratios:

Evaluate how efficiently the build system executes incrementally versus from scratch to assess its ability to avoid redundant work.

Cache Hit Ratio:

Track how often builds and tests use cached results to understand caching effectiveness and identify areas for improvement.

Flaky Test Rate and Retry Impact:

Measure how often tests fail intermittently and require reruns to pass, as these contribute to wasted compute and longer feedback cycles.

CI Telemetry:

Capture detailed timing data for each step in the build and test process, and visualize these metrics over time to identify bottlenecks and performance regressions.

Breaking through the Bottlenecks

Reducing Build Overheads in Complex Codebases:

Symptoms of this problem include long rebuild times, even after small commits, slow CI performance, and wasted resources. To address these issues, it’s important to

• Fine-grained build targets: Break the build into smaller, independent targets so only affected parts are rebuilt.
• Clear dependency boundaries: Define explicit dependencies between modules to avoid hidden coupling.
• Avoid circular dependencies: Prevent cyclic relationships that force unnecessary rebuilds across modules.
• Modularization: Split the codebase into logical, independent modules, isolating frequently changing code from stable components.

• Continuous build auditing: Regularly analyze build graphs to identify “hot paths” or modules that cause large rebuild cascades.

Smarter Caching

Inefficient caching often leads to builds that slow down after merges and produce inconsistent results between local and CI environments. To address these issues, you should 

• Use Content Addressable Caches: Instead of relying on file paths or timestamps for caching, these caches use a hash of the provided inputs such as source files, dependencies, and configurations

• Remote Build Caches: Use remote build caches that are shared among all developers and CI systems. This way, no one has to rebuild everything from scratch when their local cache is lost.

• Hermetic Builds: Hermetic builds are fully self-contained and produce identical results regardless of where or when they run. To enable this, one must pin dependencies, version compilers, and sandbox build steps. Pinning dependencies ensures the same versions of dependencies are used, versioning compilers guarantees consistent behavior and output, and sandboxing isolates each build step from the external environment, making it depend explicitly on the provided inputs.

• Monitor Cache Hit Ratios: This helps evaluate how well caching is performing. By tracking this metric, we can detect when caching becomes inefficient and take corrective actions such as pinning dependencies, adjusting cache keys, or fixing other non-deterministic behaviors.

Build Incrementally

So many times in my life, I’ve been frustrated by how long it takes to make even a single-line change. Much of that frustration comes from the fact that full builds and test runs are often lengthy, exhaustive, and time-consuming.

• Implement Change-Based Builds: Adopt a change-based strategy for both builds and tests. Rather than rebuilding everything, define fine-grained targets, detect exactly what changed, map those changes to affected targets, and rebuild only what’s necessary. With modern tools like Bazel, Buck, and Pants, this approach is entirely feasible and highly effective.

• Dependency Mapping: Build a dependency graph that captures relationships among modules, libraries, and services. Use this graph to drive intelligent build selection, ensuring that only the components impacted by a change are rebuilt. This enables incremental builds, reduces redundant work, and prevents unnecessary rebuilds of unrelated areas ultimately improving efficiency and developer productivity.

• Test Impact Analysis: Use test impact analysis to identify which tests are relevant to specific code changes. It helps with mapping tests to functions, classes, modules etc. The goal is to identify and only run tests which are relevant.

• Use Code Ownership: Code ownership maps define which sections of code is owned by which teams, by cross referencing these maps, only components corresponding the owners and the associated dependencies are built.

Optimizing Test Coverage and Execution Efficiency

When there is redundancy in test suites, test runs grow linearly with the number of commits, logic is duplicated across test cases, and excessive time is spent on end-to-end testing. To make improvements, you will have to do the following:

• Analyze Test Coverage: Periodic analysis of test coverage helps identify the extent of duplicate coverage, where multiple tests at different levels validate the same logic. Remediation through consolidation not only reduces execution time but also decreases maintenance effort and improves overall test suite quality.

• Prioritize Unit Tests > Integration Tests > End-to-End Tests: The cost of running tests increases as you move from unit to integration to end-to-end (E2E) tests. Typically, these tests become slower, more brittle, and more resource-intensive toward the right of this spectrum. Use E2E tests sparingly, reserving them only for scenarios that truly require full-system validation, and shift as much logic as possible to unit or integration tests. Always prioritize unit tests whenever feasible they are the fastest, most reliable, and most efficient form of testing.

• Pre-merge and Post-Merge Tests: Define test suites that run the minimal set of tests required to build confidence at each stage. Typically, pre-merge test suites should be fast and efficient, while post-merge suites should be thorough and exhaustive. This separation provides the necessary balance between speed and coverage, enabling teams to move swiftly while maintaining confidence in code quality.

• Intelligent Test Selection: With an understanding of dependency graphs, change impact analysis, or test-to-code mappings, test selection heuristics can be applied to run only the tests relevant to the specific change being vetted. This can be very useful for speeding up test execution and avoiding resource wastage.

Scale your Infrastructure

Slow builds and tests are big factors in slowing down developer productivity. You should invest in several options for fastening the builds and test runs, as they lead to huge multipliers in reducing cycle times:

• Use Remote Build Executors: Introduce capabilities for execute builds and tests in distributed infra, when the compute on the host falls short, the execution can easily fanout and make more infra available for completing the task.
• Introduce Auto scaling for CI Infrastructure: Auto-scaling infrastructure dynamically provisions compute resources based on workload demand ensuring optimal capacity without manual intervention. Also if you use ephemeral environments, dynamic scale in/out is must have for the infrastructure.
• Warm Pool Strategies: When new build or test environments spin up from scratch, they often spend significant time pulling base images, installing dependencies, or initializing caches. Warm pool strategies maintain a small pool of pre-initialized runners or containers that can immediately take on jobs, reducing startup latency from minutes to seconds.

Conclusion

By measuring what matters, eliminating inefficiencies, and adopting smarter strategies such as incremental builds, effective caching, modular design, and scalable infrastructure, teams can reduce cycle time and significantly enhance developer productivity.

I see this as an ongoing journey, not a destination. It takes years to build the right foundations and make progress in improving build and test speeds. We’ve come a long way, but there’s still so much more to do.

Faster feedback loops drive better code, happier developers, and quicker delivery. Ultimately, faster build and test loops amplify their impact and lead to a better developer experience (DevX).