Writing efficient, error-free, and cost-effective software that meets client requirements is hard. One of the most effective ways of achieving these goals is by understanding and embracing automation in the software development process. Automation helps streamline development by reducing developer workload and providing a standardized structure to your development processes. There are a lot of exciting opportunities and competing ideas about precisely what and how automation should be used in the software development process.
Why Automate Software Development
A lot of what defines software development practices today is ultimately about speed. CI/CD, agile, AI, and DevOps are all slightly buzz-wordy terms, but they all relate to software development methodologies that emphasize creating and deploying code quickly. Modern software development life cycles (SDLC) are short as teams compete to deliver and refine their product in a crowded marketplace.
It‘s no surprise then that teams looking to shorten their SDLC and time-to-market on new features are looking to software automation to help save time, cut costs and improve efficiency. A lot of aspects of software development are highly repetitive and time-consuming, so it makes sense that these areas are some of the most popular candidates for automation.
Pain Points of Software Development
Teams working together to develop software need to have agreed-upon standards and coding guidelines to work to, particularly if those teams are working remotely. Inconsistency in naming standards, the use of camelCase in variable names, tabs vs. spaces, and more are all small differences that can result in code smells and tech debt further down the line if each developer makes their own decisions regarding best coding practices.
Teams can enforce linters on code review to highlight these kinds of small-scale coding practices, but linting only helps with code formatting. Larger programming issues, such as software architecture, conflicting dependencies, and overlapping software libraries, aren’t as easily caught, but they can introduce inefficiencies in development and performance.
Contemporary software is highly dependent on many third-party software libraries, frameworks, APIs, and micro-services to function. Depending on how tightly coupled these dependencies are, the deployment process can be a nightmare if there are differences between the production environment and the testing, staging, or development environments. With many developers working remotely, there needs to be a standardized environment and build process that developers can easily spin up while working.
What Can & Should Be Automated
Modern software development typically utilizes distributed version control software (VCS) with a build process that is run on deployment. This build process is ideal for automation and may even be containerized. This build process can and should be automated – developers should be able to obtain the latest version of your software simply by accessing the VCS repo and running the build script. The same should be said of deploying new versions to production environments – the fewer manual steps involved, the better for everyone, as processes with manual configuration steps risk human forgetfulness and burnout. Depending on what gets missed, it’s not always obvious if there is an issue at first.
Not only should the build process be automated, but so should the testing process. It’s another area of software development that benefits greatly from standardization and automation. Tests are vital to maintaining software integrity & user onboarding experience, but manual testing is also prone to burnout and forgetfulness. Tests need to run quickly to minimize the lag between developers committing code and receiving feedback. Tests that take too long to run risk being skipped, increasing the chances of defects not getting caught. Simply running your test suite automatically on each commit or merge is not enough in CI/CD environments that may see dozens of commits every day. Teams need to get smarter about what gets tested and when in order to reduce test runtime without sacrificing test quality.
What to Expect in the Near Future
Software testing is a prime candidate for a more innovative approach to software development automation. It’s an area that is critical for preventing defects from reaching consumers, but it is costly, time-consuming, and repetitive. These are all factors that make software testing automation appealing. The future of test automation goes beyond simply automatically running test suites against new commits but involves being smarter about what gets tested and when.
Monolithic test suites, particularly regression testing, can become a significant pain point for sufficiently mature software. It seems like each test is vital to run, but over time the testing runtime will inevitably go beyond acceptable values if you are simply throwing the kitchen sink against every commit. Teams are increasingly turning to artificial intelligence to help decide what gets tested and when. If code analysis reveals a particular commit only impacts a small selection of modules, why run an entire test suite against it instead of only running tests against those select modules? These processes help reduce test runtime and return feedback to developers much faster, reduce downtime, and remove lag between code submission and feedback.
Artificial intelligence can also help identify what needs testing in your codebase. Analysis of live user data on your service can reveal exactly how your users are actually using your software. This allows you to build tests based on real use cases rather than use cases imagined by QA. Over time, user data can also reveal patterns of behavior that teams can use to streamline software testing and perform predictive analysis to determine how users will interact with novel features before they are even released.
As teams get more familiar with software automation and artificial intelligence, we will inevitably see more automation used in various aspects of software development and testing. Developers are very creative, and they’re good at problem-solving, but ultimately they are human. Smart use of automation and artificial intelligence allows teams to identify and offload the boring and repetitive tasks while allowing developers to focus on the novel and creative aspects of software development.
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Author:Â Erik Fogg is a Co-Founder and Chief Operating Officer at ProdPerfect, which is an autonomous E2E regression testing solution that leverages data from live user behavior data. |
