AI has affected multiple fields.
Its influence in the world is continuously expanding.
In fact, according to Grand View Research, the AI market size is projected to grow at an annual growth rate of 36.6% between 2024 and 2030. This shows that the impact of AI will continue to grow.
In manufacturing, AI is used to predict machine failure, this facilitates predictive maintenance and helps to reduce downtime. Moreover, in healthcare, AI is used to offer personalized treatments.
This technology is also greatly influencing software development.
It can be used in generating code and in automating the detection and fixing of bugs. Moreover, you could use this technology to test your software.
In this article, we are going to look at five interesting applications of artificial intelligence in software development in 2024. I hope to enlighten you on some of the ways these tools can be transformational to your software development.
Let’s get started.
1. AI-Generated Code
Coding can be time-consuming.
You may frequently encounter challenging problems that may take you a long time to solve.
Interestingly, according to BCG, almost half of the surveyed respondents reported that 30% of technology projects are delayed in their companies. This could increase the budgets of these projects.
You may be required to complete these projects in very strict deadlines. This may lead you to rush and make errors. Resolving the errors could also be time consuming.
Due to the strict deadlines, you may face burnout.
Additionally, as the project continues to grow it may be challenging to maintain clean code.
What is the solution?
Utilize AI-generated code.
The AI models have been trained on vast data sets of publicly available code. From this, they are also able to write code.
These tools can improve your productivity because you spend less time writing code as the AI systems are able to write most of the code. This application of AI in software development thus frees you to perform other tasks.
Moreover, AI could help in improving the quality of your code. The AI tools could suggest optimal coding patterns and best practices to use. This could potentially help you write cleaner code.
BT, a British telecommunications company, had to contend with a lot of repetitive tedious coding.
By leveraging Amazon’s generative AI, BT was able to automate 12% of its workload in 4 months. It was able to write 100,000 lines of code in this time. AI helped reduce the repetitive and time consuming work.
2. Automated Testing
Facing challenges in software testing?
You may be under pressure to provide error-free software. Your company may have invested a lot of money in the testing.
As per Statista, senior technology officials reported that 23% of their companies’ IT budget was directed towards testing. This clearly demonstrates the importance of software testing.
You may be under tight deadlines that limit the time you have to conduct thorough tests. Moreover, it may be challenging for you to produce a test environment that replicates the production environment. This could lead to erroneous test results.
This application of AI in software development is advantageous because it could help you to improve testing accuracy and speed.
The AI tools are able to study the software and generate suitable test cases. The models are able to track user patterns and study historical information to create relevant test cases.
Additionally, you could utilize AI to prioritize tests. This could help in eliminating unnecessary tests ensuring optimal resource usage. This also ensures that high risk issues are resolved.
Moreover, you could use AI to conduct self healing testing. This is where AI is able to follow a planned test script to assess the software and adapt efficiently to minor changes in the web elements.
Optimizely, an American software company, was having issues of slow and low testing coverage.
By using Cypress to automate their tests, Optimizely was able to record 4 times faster test runs. They were also able to increase feature coverage by 40%. This was because the testing was automated.
3. Automated Bug Detection and Fixing
Struggling to detect bugs?
Bugs are extremely costly.
According to CISQ’s 2022 report, the cost of software related problems has risen to $2.41 trillion in the United States. Bugs could substantially eat into your profits.
Detecting and fixing bugs can be a really cumbersome process. It may require you to sift through many lines of code and run the debugging tools many times to be able to resolve issues.
Moreover, some bugs only occur in certain environments or conditions that are very difficult to replicate. For example, the bug may only appear when there are a number of processes operating simultaneously.
AI can be used to identify bugs automatically. It could also be used to recommend and execute potential solutions.
AI models are able to detect bugs by performing static analysis. Static analysis is the process of studying computer programs without executing them.
Another feature of this application of AI in software development is that it flags potential issues instantaneously as you code.
AI systems study a lot of open source code and bug databases. From this information the systems can be able to identify code smells such as duplication and poorly formulated functions.
Regina Maria, a Romanian private healthcare network, could not be able to diagnose the issues affecting their web portal on time.
By utilizing RevDeBug, Regina Maria was able to decrease loading times on their portal by 20%. RevDeBug was able to direct the technical team to the exact code snippet where the issues arose from.
4. Predictive Analytics in Project Management
Want to give accurate project estimates?
Your superiors may require you to give accurate information on the duration and budgetary requirements of the project.
According to Zipdo, only 40% of projects meet the budget, scope and time estimates. This unpredictability could greatly disrupt your company.
You may also be required to determine potential risks. Moreover, you may want to plan on the size of your team.
If you get this information wrong, it may significantly affect your ability to reach the project objectives. Furthermore, this may reduce the trust the stakeholders have in you.
You can use the AI tools to pinpoint common risk factors. You can therefore act proactively to prevent these problems from affecting your progress.
In addition, you could use these systems to accurately predict how long it will take to achieve key milestones in the project. You are also able to accurately predict the budgetary requirement for the project.
The AI tools are able to predict these parameters accurately by studying many projects and drawing patterns from the information analyzed.
With the help of AI you are therefore able to present data driven information to the stakeholders. This is a good advantage of this application of AI in software development.
Siemens, a German multinational technology company, was having trouble in predicting the duration and cost of its projects.
By leveraging AI, Siemens was able to efficiently predict the cost and duration of projects. This enabled the tech company to be able to plan itself adequately as it was able to allocate the right resources to projects.
5. AI in Requirements Gathering
Struggling in requirements gathering?
The stakeholders may be having ambiguous requirements. You may therefore misinterpret what they mean. This may cause the project to fail.
Interestingly, according to Info Tech Research, errors in requirement gathering are responsible for 70% of project failures. It is therefore critical to adopt effective requirement elicitation techniques to ensure clarity and alignment.
Moreover, different stakeholders may have varying requirements and they may fail to agree on the path to follow.
Additionally, the shareholders may have unrealistic expectations because they might not fully understand the technical aspects of the software development process.
You can utilize AI to help in requirement gathering.
AI tools can extract critical requirements from emails, documentations and minutes of meetings. This quickens the process as you do not have to go through all this information.
Moreover, you could use this application of AI in software development for sentiment analysis. These tools can assess comments and feedback and help to rank requirements based on the priorities of the stakeholders.
In addition, you could use chatbots to interact with the users to obtain valuable information about the software. You could also use AI to transcribe meetings and provide outlines of the discourse that took place.
SAP, a German multinational software company, was having challenges in gathering and summarizing requirements for S/4HANA.
By leveraging AI, SAP was able to streamline the requirement gathering procedure. The AI system was able to automate the collection of the requirements from the different stakeholders and analyze the documentation to pinpoint the major issues.
Conclusion
AI has significantly affected software development.
AI is seen to increase productivity in software development by 20 to 45 percent according to Mckinsey & Company. This clearly demonstrates the importance of AI in software development.
The main ways in which artificial intelligence is adopted in software development are in generating code, in testing, in automating detection and fixing of bugs, in extraction of requirements and in predicting cost, duration and potential risks.
The influence of AI in software development will continue to grow in the future. This is because of the increased research in this field.
As this continues to happen, you could contribute to the conversation. How do you see AI affecting software development in the future?
I hope this article on the application of AI in software development has opened your eyes to the incredible ways this tool can transform your enterprise.