Automation in Software Development: A Complete Guide

Companies can’t rely on manual coding and testing alone when facing tight deadlines and relentless competition. Consider this: almost every company relies on automating software development and IT operations in some way or another to meet these challenges.

To be exact, about 78% of GitLab’s Global DevSecOps Report survey participants use (or are in the process of implementing) artificial intelligence (AI) automation in software development—a 14% increase from 2023. Plus, 67% of the respondents stated that their SDLC (software development lifecycle) is mostly automated.

This article explores the practical applications of automation across different facets of software development. We’re going to consider the key applications, measurable benefits, and emerging trends. Finally, DevCom will share the tools we use for software development automation and tips on how to apply them.

Software development automation refers to specialized tools, scripts, and AI platforms that perform development tasks that otherwise require manual effort. 

Modern automation technology can write, test, and analyze code in much the same way as a developer would. It even helps identify errors and inefficiencies that can slip through the cracks.

For example, 73% of respondents to the State of Testing Report by PractiTest said that automated solutions reduced manual efforts (19% said that automation had replaced over 75% of manual tests). About 25% of Google’s code is AI-generated.

Despite its popularity, automation in software development won’t replace human developers and testers. Instead, it’s meant to amplify their productivity and “outsource” the most mundane work.

Ongoing advancements in AI and machine learning (ML) have boosted the potential applications of automation tools.

Automation ideas in software development span most of the software development lifecycle (SDLC). Let’s take a look at the most common applications:

• Code generation. Scripts, templates, large language models (LLMs), and other AI-powered tools can produce entire code sections. This function is often reserved for tedious sections, like boilerplate functions, database interactions, and routes.
• Low-code development. Low-code and no-code development platforms allow non-tech workers to create and update applications through an easy-to-use graphical interface with pre-built components, templates, and drag-and-drop capabilities.
• Human-assisted coding. Code assistants provide context-aware suggestions that help developers discover new functions. It’s especially useful for junior developers unfamiliar with coding practices or APIs.
• Testing and QA. Testing frameworks can run unit, integration, performance, and other tests to identify bugs and security issues. Tools like Selenium help quality assurance teams verify end-to-end scenarios, usability, and reliability.
• Continuous integration and deployment (CI/CD). Automation in software engineering helps test and verify the code changes from multiple developers before being released into a shared repository. A DevOps pipeline might include automation tools that test each commit before deployment. 
• Infrastructure as code (IaC) provisioning. IaC tools streamline server, networks, and container configuration. Developers can launch or update complex server clusters consistently in multiple environments.
• Documentation and code comments. In what may be the most cumbersome part of the work, these tools can maintain consistent documentation from code changes, in-line comments, API definitions, and docstrings. Some models help create and refine code comments that explain functions and architectural decisions.

Best of all, automating software development fields has measurable productivity advantages and business benefits.

The correct implementation of the right automation technologies changes how you deliver software. It offers several important benefits for your engineers and business as a whole:

• Offloaded repetitive tasks. Automation tools take over low-complexity tasks, like generating test data, bug identification, code reformatting, and documentation. Though rudimentary, these tasks consume too much of your team’s time and energy and are better redirected elsewhere.
• Improved efficiency. Automated tools with AI and ML lead to a more rapid development cycle and increased productivity (per the Research and Markets 2024 report). Over 51% of companies expect generative AI to improve their test automation efficiency, with 41% believing it will help produce more complex and diverse test cases.
• Faster time to market. Automation dramatically reduces development cycle times by streamlining critical processes like testing, integration, and deployment. According to recent studies of LLMs, AI automation can reduce the development time by 40%-51% (depending on the scenario).
• Cost savings. Automated and AI-assisted processes reduce engineering and testing hours, reducing organizational expenses over time.
• Enforced consistency. AI tools can enforce consistency and precision across testing, deployments, and documentation tasks. When used smartly, automation technologies will reduce the risks of errors often caused by manual work.
• Higher team satisfaction. Teams get more satisfied, productive, and innovative as they delegate mundane tasks to automation tools. The result is more efficient engineers who don’t burn out.
• Enhanced security. Embedding automated security checks into the development pipeline (the concept known as DevSecOps) helps teams detect and rectify more security vulnerabilities before release.
• Streamlined deployment and maintenance. Automated software development tools handle release testing, log analysis, patch management, and performance monitoring without constant input. Using AI for IT operation management can reduce unplanned downtime by 20% (per Gartner).
• Improved scalability. Automated workflows can scale to handle larger workloads, new projects, and increased complexity. In other words, companies can maintain efficiency without needing proportional staff or resource increases.

Despite these many advantages, automation can pose several technical and oversight complications.

There are technical limits and non-technical problems presented by automation in software development. Below is a detailed explanation of the key challenges and practical solutions.

• Inconsistent accuracy for coding tasks

  Outputs generated by AI tools for some coding tasks can require debugging, adaptation, and refinement. Unlike more mundane processes (like testing, verification, formatting, and documentation), there’s no way to fully automate coding yet. Other than that, automation tools learn from vast datasets that may contain outdated practices, biases, or vulnerabilities.

  Modern automation tools still require complementary actions and monitoring to ensure accurate and practical application of the generated code. You should pair automation tools with human oversight to validate outputs. However, they have semi-automated workflows that can still reduce development time.

• Code plagiarism issues

  Automated tools, especially SaaS solutions like ChatGPT API, rely on shared training datasets. The software generated with tools that use these datasets increases the likelihood of “stealing” code from other applications (or generating extremely similar code), which may lead to intellectual property issues. 

  Review and audit automated code for originality before deployment. Additionally, plagiarism code detection tools can scan your code for similarity with existing software.

• High initial setup costs

  Automation technologies can be costly to implement in your workflow due to infrastructure requirements, licensing fees, configurations, and lack of skills. Besides, not all aspects of the development process warrant such tools.

  Begin with a cost-benefit analysis to identify high-impact areas for automation. Teams with limited budgets can start with pilots and open-source tools. Once savings accumulate and the ROI becomes apparent, you can allocate more funds for further automation.

• Integration complexity and compatibility

  Software development automation tools can be incompatible with APIs, deployment pipelines, or coding standards. Without proper planning, these integration challenges can lead to fragmented workflows and data silos.

  Before adopting any automation tool, evaluate its compatibility with your current tech stack. Perform a proof of concept (PoC) to identify and address integration gaps early.

  It’s worth trying to partner with experienced software development companies to tailor solutions for complex systems.

• Resistance to change

  Some developers fear that AI tools may replace their roles, while others struggle to adapt to new workflows. Additionally, a lack of familiarity with the tools may lead to errors or inefficiencies in their application. About 42% of testing leaders still don’t feel comfortable writing automation scripts for testing.

  You should communicate the supportive role of automation tools in software development. Highlight that AI tools require human oversight to ensure code quality and align with project requirements.

  Meanwhile, provide training programs and workshops to help your team get more confident with tools like GitHub, Tabnine, and ChatGPT. Conduct regular performance reviews to show how automation improves team outcomes.

• Cybersecurity and compliance risks

  Automated pipelines can introduce new vulnerabilities. For example, if access controls or encryption are not configured properly, you can expose sensitive data during deployment. It’s possible that the automated systems will use your customers’ personally identifiable data for testing, which can break compliance regulations.

  Adopt a DevSecOps approach, embedding security into every phase of the automated workflow. This includes automated security scans at every stage of the SDLC, role-based access controls for your databases, and encryption protocols for sensitive data. Additionally, you should use anonymization or synthetic data generation tools to produce privacy-compliant data for testing.

• Long-term maintenance

  Neglecting maintenance can lead to inefficiencies or even disrupt critical workflows. Automated tools require regular correction and adjustments, so they don’t flag correct behavior as bugs or fail to adapt to your coding standards.

  Establish a system of continuous feedback and monitoring. Review system performance metrics regularly and gather user feedback to identify bottlenecks or issues. This will help design automation scripts with modularity, allowing components to be reused across environments and updated without overhauling other automated workflows.

On another note, most of these challenges can be avoided if you select your automation tools wisely.

Modern automation capabilities can streamline more than just mundane tasks. Below, we’ll explore a few tools we use at DevCom for specific aspects of development.

• GitHub Copilot: An AI coding assistant powered by OpenAI that proposes coding lines, function implementation, and algorithm outlines based on the programmer’s coding style. It can also review an existing codebase to maintain consistent standards across projects.
• GPT API Chat: LLM-based interface for code generation. Additionally, the model can automate documentation updates, debug processes, or even create CSV analysis.
• Amazon Q: Amazon’s ML capabilities can generate large sets of realistic input values for unit and integration testing, as well as AI recommendations for code optimization.
• JetBrains AI Assistant: Identifies code issues (like code smells or repeated logic) and suggests immediate refactoring options to improve clarity, design patterns, and performance. The tool can also recommend test cases based on the code flow.
• Framer X: A design and prototyping tool that can convert design elements into React components with interactive animations, helping produce functional prototypes. 
• Tabnine: ML-powered autocompletion tool that offers code suggestions for multiple programming languages. This tool allows organizations to train the model on-premises, helping comply with strict data privacy regulations.
• Microsoft Power Apps: A low-code platform for building functional prototypes and applications with hundreds of connections for data flow automation. It also integrates with other Microsoft solutions, like Azure, Office 365, and Dynamics 365.
• Selenium: Open-source frameworks for scripts that automate various web browser interactions (user action, page content, data display, etc.).
• Jenkins: A CI server that manages build, test, and deployment pipelines for automatic code compilation.

These tools each focus on automating different facets of software development. However, the number of applications will increase in the future.

AI models are continuing to get more accurate and handle more intricate tasks, which will gradually make more aspects of software development automated. Below are trends to watch out for in the coming years.

• Advances in generative AI

  Advanced language models are getting better at understanding complex codebases, suggesting optimizations, identifying potential issues, as well as automating code generation. According to R&M’s 2024 Generative Artificial Intelligence in Coding report, the market for generative AI in coding will increase more than threefold from 2024 to 2029 (from $35.53 million to $110.8 million).

• AIOps for change risk analysis

  Organizations will increasingly rely on the use of AI, ML, and generative AI to resolve common issues in IT environments. The global market for AIOps is set to reach $8.64 billion by 2032 from $1.87 billion in 2024, according to the Fortune Business Insight report.

• DevSecOps integration

  DevSecOps will help automate even more security tests for development, testing, and IT operations processes. Based on Grand View Research data, the global DevSecOps market is projected to reach $20.24 billion in 2023 from $8.84 billion in 2024 (with a CAGR of 20%).

• Spread of low-code platforms

  More companies will use low-code application platforms for automation of the software development process. According to Gartner’s 2024 Magic Quadrant report, about 80% of businesses worldwide will use low-code platforms for mission-critical app development (65% more than in 2024).

Given the complexity of some of these tools, organizations should plan how exactly to implement them into their workflow.

Organizations should understand what they want to automate in their SDLC and why. At DevCom, we approach automation initiatives just as thoroughly as other software development projects.

• 1. Identify priority tasks for automation

  For starters, identify the processes whose automation will grant the most benefits. We use diagnostics and maturity models to pinpoint which repetitive processes will yield the greatest ROI if automated.

  At the same time, you might want to keep the scope controlled. It’s best to start with smaller processes. Then, as you get more comfortable with the technologies, you can implement automation tools for more critical processes.

• 2. Analyze the requirements and choose the tools

  Assess the technical requirements of the business automation software development project. Then, develop a roadmap and framework that match your strategy and desired timing. 

  At DevCom, we arrange a series of meetings with key stakeholders to assess your company’s needs. Then, we will prepare a plan with timelines for your approval.

  Ensure the tools you select integrate seamlessly with existing platforms and can handle your workloads. If you opt for open-source software, make sure you have configured robust security mechanisms and privacy-enhancing tools.

• 3. Design a PoC

  Before full-scale implementation, DevCom emphasizes designing a PoC to validate the feasibility of the automation tools and workflows. Test how well the tools integrate with the existing tech environment, interact with CI/CD pipelines, and handle real usage scenarios.

• 4. Pilot rollout

  To minimize risks, DevCom begins automation with a controlled pilot rollout in a limited repository or staging environment to monitor its impact. We measure key performance indicators (KPIs), such as time saved, error reduction, or code quality improvements.

• 5. Deployment and integration

  Once the pilot phase demonstrates success, we can deploy the automation setup across the organization. We use monitoring tools to track system performance and ensure that automation runs smoothly.

• 6. Iterative improvements

  Automation in software development requires ongoing maintenance updates and corrections. You should continuously analyze key metrics and manually review the output. It would help to establish channels for engineers to report issues or suggest improvements to the automation workflows.

  At DevCom, we use a variety of techniques for the automation of software development and infrastructure optimization. For similar case studies, you can see how we helped a healthcare organization streamline its workflow with custom APIs and DevOps services or how we helped a QA provider migrate to a more scalable Azure environment.

Like it or not, the vast majority of organizations are largely outsourcing major aspects of development to AI. The gradual adoption of an automated workflow can shorten the SDLC, reduce operational overhead, and detect issues. 

While challenges exist, a well-planned rollout and the right tools can make the adoption easier. 

DevCom can help you automate software development, manage infrastructure, streamline workloads, and much more. If you want to learn more, you can contact us directly.