AI Software Development: How It’s Revolutionizing Lifecycles, Testing, & More

AI and ML technologies are being increasingly applied in software development and test automation to program repetitive tasks, improve code quality, and enhance the accuracy and efficiency of testing. By leveraging AI, software development teams can accelerate their development and testing processes, reduce errors, and deliver high-quality software products to market more quickly.

AI in software development & testing

AI tools have become increasingly critical for software development and testing over the past few years, and for good reason. Advancements in AI systems have moved them from the theoretical to practical, assisting everything from early project planning to final deployment and ongoing upkeep. Right now, AI is largely used for the automation of repetitive tasks, but has also improved the overall quality and speed of application creation and delivery through code generation and optimization. More recently, AI models that incorporate reasoning or deeper analytical capabilities are able to provide better insights into software progress and effectiveness.

However, the major overlap between AI and software development in the last two to three years has been code generation. LLMs have been remarkably efficient at understanding syntax and formatting of different coding languages, making it much easier to use natural language coding (or no-code testing and development) to create applications. While this started out fairly basic, AI platforms like ChatGPT and Claude Sonnet are rapidly improving to perform more complex coding tasks and test automations.

We believe that this growing relationship between AI and software development comes down to their shared dependence on data and logic. In other words, software development provides structured inputs (like code, test results, logs, etc.) on which AI thrives. In turn, AI then brings speed, scale, and prediction capabilities that go beyond what humans can do, creating a feedback loop of improvement.

Impact of AI on software development

Before we dive deeper into the impact that these shifts have had on software development, it’s important to first take a step back and look at the tech industry as a whole. The emergence and growing influence of AI and ML can be attributed to several factors. First, the explosion of data in recent years has made it necessary for businesses to efficiently extract insights from this vast amount of information, which AI algorithms have proven to be very effective at. Computing power advancements have also made it easier and more cost-effective for companies to implement AI technologies in their operations. Open-source libraries and frameworks make it easier for developers to get started without requiring extensive knowledge of machine learning algorithms and techniques. This emergence and growing influence of AI tools, particularly in the tech industry, is driven by the need for businesses to remain competitive in a rapidly changing landscape and the increasing availability and accessibility of the technology.

Now when it comes to AI’s impact specifically on software development, we’ve seen several tangible shifts in how people work and deliver products.

• AI Pair Programming: Pair programming with tools like Copilot have revolutionized how developers write and optimize their code, offering suggestions and fixes that may be overlooked by humans.
• Maturity of AI-Assisted Testing: As software testing tools increasingly leverage AI (like Rapise) and improve on its effectiveness, a wider variety of activities can be handled by automated systems, reducing bottlenecks in the SDLC.
• Shift in Developer Focus: Automation of test case generation, test execution, documentation, and more has freed up developer time to focus on more valuable areas like better prompt engineering, more complex problem solving, and architectural design.
• Rise of No-Code/Low-Code Platforms: The lower barrier to entry has resulted in more applications created by those with minimal programming knowledge. While we believe this is a positive shift, it’s critical to thoroughly test these platforms for security vulnerabilities and other issues that may be overlooked by someone without a development background.

Benefits of AI in software development

These industry shifts as a result of AI’s impact on software development offer many advantages to those who adopt these cutting-edge tools:

• Faster Development Cycles: The efficiency improvements of AI automation tools have significantly sped up development cycles by reducing time spent writing boilerplate code and executing repetitive tests. This has enabled development teams to iterate faster, release features more often, and react more rapidly to market demands.
• Better Code Quality: Not only is code created more quickly, but it can often be higher-quality because of AI’s analysis capabilities to quickly scan for potential issues or security vulnerabilities in real-time.
• Reduced Costs: The overall efficiency and quality improvements translate to reduced costs of reworking faulty code and fixing bugs that were caught late in the SDLC. The faster time-to-market also provides a significant competitive advantage.
• Wider Test Coverage: AI-powered test generation using tools like Rapise enable more comprehensive test coverage (including edge cases) without sacrificing test quality, which improves QA effectiveness and confidence in the product’s reliability.
• Smarter Decision-Making: The unmatched efficiency of AI’s data analysis capabilities facilitates more informed decision-making for things like architectural recommendations, delivery estimates, performance regressions, and risk management tactics.
• Improved Developer Experience: As mentioned above, freeing up developer time by automating mundane tasks allows them to focus on more challenging and creative areas of their work, pushing them to learn more and develop their problem-solving skills. This makes better developers who are often more satisfied with their jobs.

Challenges of AI

However, while AI clearly provides many benefits to software development and testing, there are also hurdles and drawbacks that need to be considered:

• Data Quality: These algorithms rely on large amounts of high-quality data to learn and make accurate predictions. However, it can be challenging to ensure that the data used for training models is representative, unbiased, and free from errors or anomalies.
• Bias and Ethics: Algorithms can also perpetuate and even amplify existing biases in the data used to train them, which can have significant ethical implications. It is essential to ensure that these systems are designed and implemented in a way that is fair, transparent, and ethical.
• Over-Reliance on Automation: While AI can automate many repetitive and time-consuming tasks in software development and test automation, there is a risk of over-reliance on automation. This can lead to a lack of human oversight and judgment as well as skill degradation, so it’s essential to find the right balance between automation and human input.
• Security and Privacy: Some systems may be vulnerable to cyberattacks and data breaches, which can compromise the security and privacy of sensitive information. Because of this, implementing robust security measures to protect AI-written code from these risks is crucial.
• Explainability: Current AI models are often “black boxes,” which makes it difficult to understand how they came to a certain conclusion or generated a specific piece of code. This lack of interpretability can be an issue for software development in fields like healthcare, aviation, and other industries where safety is at stake.
• Integration Complexity: We’re currently in a transitional phase as development teams begin to adopt AI tools into traditional pipelines. This has made it clear that adopting and incorporating these radically different functionalities will not be a seamless experience, especially for teams with legacy systems or very entrenched practices.

How to use AI in software development & testing

Despite these challenges, AI systems have become increasingly essential tools in software development due to their benefits — namely, the ability to improve the efficiency, quality, and accuracy of the development process. So, how can you use AI-driven software development tools to avoid the pitfalls discussed above while maximizing the value they provide?

Before taking action to incorporate AI, it’s important to begin by identifying high-friction or repetitive areas in your SDLC that would benefit most from efficiency improvements. From there, we recommend following these overarching best practices:

• Explore Available Tools: Research and evaluate the various AI-powered software development tools, considering factors like ease of integration, features, pricing structure, etc.
• Start Small: We discussed the difficulties of integration above, so start by implementing AI in specific, well-defined areas, monitoring and gathering feedback from your team to iterate your approach based on this input.
• Provide Training: Another way to mitigate adoption struggles is to offer plenty of support (documentation, workshops, ongoing guidance) so your team can make the most of these new tools and understand their value.
• Augment, Don’t Replace: The goal should be to help your developers, not replace them — emphasize how AI-powered tools can help them focus on more strategic and creative activities instead of tedious ones.

Now that we have a foundation of considerations as you begin to implement, what are the key areas and examples of how AI can help your processes?

Requirements gathering

Starting at the beginning, AI can parse through emails or messages from stakeholders to extract user stories, acceptance criteria, and more. Natural language processing (NLP) is a major functionality to speed up requirements gathering and organize documentation without spending hours of manual searching to create these documents. For new features of an existing platform, AI can quickly perform sentiment analysis on survey responses, product reviews, and social media to identify areas for improvement and proposed changes. This valuable user insight refines your requirements, setting the project up for success.

Taking requirements a step further, AI reasoning models are increasingly capable of effectively analyzing and prioritizing requirements based on a multitude of factors. These algorithms can take things like business value (based on historical project data), technical feasibility, and dependencies to efficiently manage and recommend which requirements are most valuable or important.

Code generation

AI can quickly generate boilerplate code, function skeletons, and language translations (like Java to Python) to get projects off the ground sooner. This can be done from a variety of inputs, including natural language descriptions of the required functionality. For example, some programs are able to take a prompt of “a function that sorts a list of integers” and output clean code in Java or Python to perform this task.

Beyond initial generation, AI-powered assistants and tools can suggest (or even complete) blocks of code based on the existing context, coding style, and designated best practices. Not only does this further speed up the process, but also reduces the likelihood of typos, syntax errors, and style inconsistencies.

Refactoring & optimization

Building on the code generation step, AI tools can be used to refactor existing code to maintain functionality but streamline and clean up codebases that have grown out of control over years of expansions. Even general AI chatbot tools like ChatGPT, Gemini, and Claude have gotten incredibly adept at detecting code smells and suggesting ways to restructure legacy code while minimizing disruptions. By flagging inefficient loops, redundant logic, or potential concurrency issues, AI coding assistants can help significantly improve performance of a system (including in ways that human developers may not have thought of).

Test automation

Probably one of the most common implementations of AI in software development right now (and a major way that Rapise and SpiraTest leverage AI), test automation has taken a huge step forward. Not only can platforms like SpiraTest auto-generate test cases from requirements or code, but they can identify gaps to suggest additional areas to evaluate. Rapise’s self-healing tests further enhance your testing activities, using AI and advanced optical character recognition to keep your tests functional, even when the application’s UI changes.

No-code features again reduce dependency on manual scripting of tests. Using plain English and visual interfaces, AI tools can turn natural descriptions and inputs into test scripts and run them, which makes software testing more accessible. Analysis based on historical test results can also tip off AI algorithms to which areas present the most risk of failure after a code modification, focusing testing efforts in these areas to save time and resources.

Bug detection

AI can help improve bug detection in two primary ways:

• Predictive Bug Detection: Analysis of code to identify areas that might have vulnerabilities, anomalies, and patterns that could lead to bugs.
• Root Cause Analysis: Analysis of error messages, logs, code changes, and more after a bug is reported to identify where the bug actually came from.

Both of these assist in reducing costs, either by resolving bugs before they become issues or getting to the root of identified bugs more quickly, saving time and resources.

Documentation

One of the more tedious aspects of software development and testing, documentation is still incredibly important for improving processes in the future. AI can help generate inline comments, summarize changelogs, and generate onboarding documentation tailored to each stakeholder. This vastly reduces manual effort in compiling information, while still keeping all team members and stakeholders informed.

Beyond basic changelog summarization, AI tools like SpiraTeam can generate detailed, real-time reports and dashboards that provide valuable insights. These could include types of failures, trends over time, and potential bottlenecks to be aware of. This again frees up more time for developers to focus on strategic and value-added tasks instead of constantly creating new reports to share with stakeholders.

Security enhancement

Security is becoming increasingly important in software development as we move towards other seismic shifts like quantum computing. Because of this, security testing now needs to be a major piece of every SDLC, thoroughly checking for vulnerabilities and preparing for a future of new threat vectors. As we’ve mentioned, AI tools can help check for weak points in real time, and some platforms can even continuously monitor third-party packages for CVEs. Once the code is ready to be tested, AI-powered testing tools can automate security testing activities (like fuzzing or penetration testing) in a scalable format across a variety of simulated platforms and environments.

Deployment

AI can not only analyze historical data to forecast deployment risks or predict rollback likelihood, but also recommend proactive strategies to mititgate these risks. AI features can further be integrated into CI/CD pipelines to automate activities and accelerate deployment timelines. Some tools can even continuously monitor live systems for anomalies that might indiciate issues or regressions post-deployment. This facilitates faster detection and resolution of problems once the application has been deployed to production.

Examples of AI use cases in software development

So what are some specific examples of how AI is used in software development and automation testing right now? You can start considering integrating AI via tasks such as:

• Test Planning: Use AI platforms to analyze requirements and historical data to suggest test strategies, identify critical test areas, and more accurately estimate testing efforts.
• Workflow Automation: Use AI tools to automate ticket updates, assigning tasks, triggering tests, and regression triage.
• Load and Performance Testing: AI-powered testing tools can better simulate realistic user behavior under variable loads for more effective performance testing.
• Test Case Maintenance: Find an AI-powered development tool with self-healing tests to maintain them and minimize tedious manual fixes when UIs or data changes.
• Code Review: Try pair programming or formal code reviews with a code assistant to help flag potential issues or suggest improvements.
• Chatbots for Dev Assistance: Beyond pair programming, an AI chatbot can be a valuable resource for developers to bounce ideas or problems off of.

Where is AI less useful in software development?

While much of this article has described the benefits of leveraging AI in your software development workflows, it’s important to recognize that there are plenty of areas where a human touch is more valuable than AI’s speed. Some areas where we recommend not using AI for your SDLC include:

• Ethical Decision-Making: Situations like balancing user privacy with functionality or determining the societal impact of a product require human moral reasoning to fully understand the implications and weigh pros and cons. AI algorithms lack the capacity for this nuanced judgment, and could make decisions that aren’t in your users' best interests.
• Abstract or Novel Problem-Solving: AI systems tend to lack the holistic system-level reasoning and creativity required for unprecedented challenges. The out-of-the-box thinking that we have as humans is critical for overcoming complex issues that AI training data doesn’t include or hasn’t been exposed to.
• Debugging Emergent Behaviors: For problems that only appear under specific, real-world conditions, experienced devs are typically necessary to untangle and resolve. This is because these issues often rely on factors like intuition, forming hypotheses, and experimentation, which AI algorithms lack.
• Complex Stakeholder Communication: Development often requires frequent interaction with a variety of stakeholders (clients, product owners, end-users, etc.), where effective communication and relationship building is critical for project success. AI cannot replicate human social skills, emotional intelligence, or understanding of interpersonal dynamics, which can hurt stakeholder communication.
• Initial Conceptualization/Vision Setting: While we’ve discussed how AI can help get projects off the ground early in the SDLC, it shouldn’t be the first step. Ideation and vision for a product need to rely on our human imagination, intuition, and ability to identify unmet needs, which can then be supplemented by AI-assisted market research, trend analysis, etc.
• Handling Ambiguity: AI models and algorithms rely on huge amounts of well-defined data to identify clear patterns and eliminate uncertainty. In situations where requirements, priorities, or challenges are ambiguous or changing, an AI tool might struggle to identify patterns and therefore struggle to provide insightful value.

Future of software development with AI

As we move into the second half of the decade, we’ll continue to see innovation around AI and software development — likely at an accelerated pace. Some trends that we’re already seeing in our industry include:

• Code Generation Sophistication: A fairly obvious trend but worth spotlighting nonetheless, the complexity and nuance of AI-generated code has been improving dramatically in the last year, and this is expected to continue (e.g. generating entire features based on high-level specifications).
• AI-Driven Project Management: Beyond the actual code, AI agents and tools are beginning to play more significant roles in project management tasks like risk management, resource allocation, automated task scheduling, and more.
• More Autonomous Testing: This has been a major area of innovation for software testing tools like Rapise and SpiraTest, and these tools will continue to become more autonomous over time. We expect these systems to not only be able to generate and execute tests, but also analyze results and identify root causes.
• Explainable AI for Compliance: The importance of understanding how algorithms came to certain conclusions or made certain decisions was discussed earlier, and the concept of XAI (Explainable AI) could help. This type of AI system is able to explain to humans the reasoning behind a suggestion or action taken, improving trust in these systems.
• AI-Powered Security: As we’ve seen security become integrated in more phases of the software lifecycle (not just the very end), we anticipate this trend to continue as AI-driven security checks are incorporated at every stage of the SDLC.

Looking further ahead into the future, some speculative trends that we might see emerge in the next 5-10 years around AI software development include:

• AI as a Partner: Artificial intelligence could become far more than just a tool, acting as a collaborator in the development process, participating in design discussions, and even autonomously contributing large portions of a system’s codebase.
• Self-Evolving Software: AI that learns and adapts could allow systems to be self-healing, automatically detecting and resolving issues without human intervention. Beyond fixing bugs, this could start extending to performance optimization or even evolving the product’s functionality based on user feedback or environmental changes.
• Further Democratization of Software Development: As we discussed with AI lowering the barrier to entry for creating software, continued innovation will make software development more accessible than ever. At Inflectra, we see this as a positive shift, focusing on all of the new creations and ideas that people can bring to life via software.

Will AI take over software development?

It has been said many times that “AI won’t replace you, but someone using AI will.” It’s important for developers and testers to start learning and adopting these new technologies and tools to keep up with the pace of innovation. Many companies around the world are already taking advantage of the benefits of AI-driven software development by integrating it into workflows, and you shouldn’t be left behind.

The importance of these technologies and their impact on modern software development can’t be overstated, which is why we offer a range of products that integrate cutting-edge tools, industry-leading test automation, and other development solutions to deliver better software faster. Our platforms help teams of all sizes and industries streamline their development and testing processes, reduce manual effort, and improve collaboration and communication throughout the software lifecycle. With Inflectra, you can leverage the power of AI and machine learning to enhance your software development and achieve your business goals. Get started with a free trial below!