When it comes to software management, developers face a variety of challenges in maintaining software quality. They can be restricted by legacy systems, hindered by complex code management and debugging processes, limited in ways to optimise performance and struggle to ensure code security.
How businesses manage and optimise their code has a direct and wide-ranging impact on their software performance. By optimising code efficiency, companies can tackle the root causes of these challenges; efficient code means greater energy efficiency and more cost savings, and it allows businesses to effectively scale their applications.
Here are four challenges in maintaining software quality and how optimising code using AI can tackle them.
Outdated code: time for an upgrade
Developers often face a battle to ensure their software is up to date with the latest versions of the programming languages they are written in. Why does this matter? Outdated code can lead to far more problems than it may first appear: it makes software applications more inefficient and unsustainable, more costly to run, more vulnerable to cyber-attacks, and it can create compatibility issues with modern systems.
AI-powered code efficiency platforms can speed up the process of modernising code to the latest version on offer and ensure that it adheres to current standards. Crucially, this allows engineers to optimise their productivity by minimising the time they spend on manually upgrading each application – a process which can eat into days of developers’ time and increase costs.
With modern codebases, companies are well placed to increase the lifespan of their legacy systems, enhance interoperability with different systems and new technologies, and strengthen their defences against cyber-attacks.
Code complexity and knowledge loss
As companies grow and evolve, so too will their codebases. These growing codebases can make it increasingly challenging for developers to find specific functionalities or to understand parts of the code, particularly when new team members are onboarded or the code has been inadequately documented.
Some of the latest AI platforms utilising LLMs have a chat interface which can understand natural language queries and offer advanced code search functionalities. Using the chat, developers can ask complex questions about the codebase, such as investigating how different code elements interact with each other, and can also search the web for the most up-to-date open-source libraries.
Crucially, the platform acts as a live documentation tool, storing updates/changes to code and massively minimising the learning curve for developers in understanding complex codebases. Using the tool, companies can foster better collaboration between developers and minimise the damage of ‘knowledge loss’ if employees leave, meaning that when new developers enter the team, they can be onboarded more efficiently.
Optimising code inefficiencies
Inefficient code can be a significant drain on resources – whether directly noticeable or not. A code section may contain suboptimal algorithms or have unoptimised data structures, meaning that the software is overusing resources and contributing to huge compute costs and scalability challenges.
If a developer were to try and manually identify these inefficiencies, it would take huge amounts of time. With an AI platform to hand, however, developers can automate the code analysis process and then optimise code performance. The technology can automatically identify inefficiencies in code bases in line with developer priorities, such as optimising run time or memory usage. It then provides a range of alternative code snippets that are able to perform the same tasks more efficiently, offering optimal improvements to algorithms and data structures.
If software is performing tasks more efficiently, it is also using less energy to do the same tasks, and so it operates more sustainably. By having more efficient code, organisations can manage their resources better and scale applications more effectively, creating a strong foundation for continued growth.
Ongoing security vulnerabilities
The world of cyber threats never sleeps and continually evolves, meaning that code has to be constantly evaluated for security vulnerabilities (including those that may have been overlooked when it was first developed). These dangers can be particularly apparent in open-source code, where vulnerabilities are more likely to be publicly shared.
Such an unforgiving landscape requires a tool that safeguards security around the clock. AI can scan hundreds of repositories to highlight security deficiencies and then suggest any code changes to bolster code bases. Not only does this ensure the software is running efficiently but it also secures it against any potential attacks, whenever they may come.
By taking a proactive approach to security, applications are set up to be robust against emerging threats, mitigating the use of time, money and resources to reactively deal with any security breaches. Crucially, this helps build customer trust and protects sensitive data.
Ensuring quality code with AI
As codebases grow in an evolving technological landscape, developers face a range of challenges in maintaining software quality and scaling applications. Outdated systems, a lack of code knowledge and documentation, hidden code inefficiencies, and cybersecurity vulnerabilities all impede code and software performance.
The modern business environment is not built for manual code optimisation processes. In fact, with technology to assist developers, businesses will be protected from security breaches, and technical disruption and find it easier to scale.
Optimising code is where AI can work to its optimum; it provides objective codebase analysis and then offers suggestions for improving the inefficiencies identified. Developers can then assess these suggestions and integrate improvements. Through this approach, businesses can drive software performance and efficiency, improving sustainability, reducing costs and safeguarding security.
Quality software has never been as necessary, and AI can help developers overcome their greatest software management challenges.
Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform - EvoML - for building and scaling AI.