Son Nguyen is the co-founder & CEO of Neurond AI, a company providing world-class artificial intelligence and data science services.
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Software engineering is no longer an unfamiliar term. Since its inception, it has adapted to new technological advancements, become a transformative force in the tech sector and bridged the gap between human intentions and machine execution.
In fact, the rapid growth of software development recently presents an enormous challenge for professionals, not only in the variety of tasks and responsibilities but also in the rise of new technologies. According to a Reveal survey (via Spiceworks), 26.2% of developers struggle to manage their workload, while 26% of them consider client's expectations to be too high.
Still, the software engineering landscape is being improved and reshaped dramatically thanks to AI technologies, especially large language models (LLMs). Rather than wasting time on the manual process, AI tools help automate everything, from analyzing to coding and testing.
But how can LLMs create such magic? This article will show how they simplify workflows, enhance accuracy and cut back on human efforts in modern software development.
History Of Software Engineering
Trace back to the mid-20th century until now, software engineering has undergone numerous transformations, aligning itself to changes in technology, programming languages and the problems that need solving.
From the late 1940s to the early 1950s, programmers wrote in binary machine code—a series of 1s and 0s, which was laborious, error-prone and hard to understand.
Assembly language replaced binary coding, making machine code more accessible. Although the code was presented in a more human-readable format, people still need help explaining and understanding.
ALGOL and Pascal languages, which were used predominately in the 1960s and '70s, formed structured programming. Developers could organize code into logical and manageable sections more easily. However, understanding program control flow increased the complexity.
The invention of JavaScript, PHP and later Ruby or Python supported web applications and dynamic websites. Engineers could build applications that performed well and scaled to accommodate user growth. Still, the challenge now lies in understanding coding practices, database management, server setup, etc.
Challenges Of Traditional Software Engineering
Despite the advent of techniques and languages, software engineering still has many challenges.
Businesses have to pay anywhere from $137,000 to $173,000 annually to hire a software engineer in the U.S. The expenses include salary taxes, benefits, and recruitment fees, not counting the productivity loss costs such as bug fixes, software failures or the lack of required expertise. These issues will affect the final product quality or lead to overshot budgets.
Software quality is also a big concern, ensuring the final product meets the stipulated requirements, is easy to use and is reliable. Achieving optimal software quality may remain challenging due to poor or incorrect program methods, leading to the software not working as it should.
Last but not least, traditional software engineering is time-consuming. How many lines of code can a developer write in a day that actually work? It’s around 300-500, on average, because of the time for developing use cases, testing and debugging. As a result, project timelines often get extended. This is particularly the case for complex and large-scale projects.
That’s where automation tools step in to improve software quality while saving time and money.
The Potential Impact Of LLMs On The Future Of Software Engineering
LLMs prove to be a game-changer in tackling these challenges. Rather than replacing developers, LLMs can work hand-in-hand with them. They assist in producing efficient and readable code lines quickly in any programming language.
AI code generation tools using LLMs like GitHub Copilot, ChatGPT or Tabnine help write code faster with fewer errors. They give engineers superpowers in generating code in numerous programming languages, from C++ to Python and Go. Thus, developing apps or software has become a piece of cake now. Regarding front-end web development, Enzyme stands out from competitors in building websites.
LLMs extend their advantage by analyzing code and spotting coding errors before they spiral into serious issues. They provide invaluable feedback on the code quality, maintainability and potential scalability issues—far quicker than any human could.
Another revolutionary feature is their application in testing. LLMs can generate test cases automatically, stressing various aspects of the software to ensure robustness and reliability. Additionally, these models can create accurate and comprehensive documentation, reducing the time developers need to spend on it.
Finally, the adoption of different programming languages has sometimes become a bottleneck. These languages have many complexity and syntax levels, requiring significant time and resources to learn and understand. Plus, your organization must deal with different software updates, patches and versions. LLMs break that obstacle by translating programming languages, promoting increased interoperability across other tech stacks.
Cautions To Consider When Applying LLMs
Every rose has its thorns. As with any technology, LLMs come with their own set of challenges.
Hallucinations, or inventing information outside the data they’ve been trained on, are common problems. This leads to unclear or incorrect instructions, causing significant roadblocks in software development efforts. Nevertheless, new LLMs are being improved. They’re trained on higher-quality data, clarify intended uses and limit responses to reduce hallucinations significantly.
Data quality and bias is another concern to deal with. As mentioned, an LLM’s output largely depends on the quality of the training data, and it can perpetuate or even exaggerate existing biases in the data. Pay attention to avoiding bias in training data and constant monitoring for such problems.
Business owners apprehend LLMs due to privacy and security issues. How can you make sure your sensitive data is not used to train the model? LLMs can unintentionally generate text that appears to contain details from a specific source or even potentially leak proprietary or sensitive information. In this case, you need private LLMs customized for your organization to prevent outside attacks.
As Yann LeCun, chief AI scientist at Meta and Silver Professor at New York University, so eloquently put it, “On the highway towards Human-Level AI, Large Language Model is an off-ramp.”
LLMs significantly impact software engineering by improving the quality of software systems. Regardless of the challenges, the potential benefits are too great to ignore. Indeed, the future of software engineering will witness a profound paradigm shift led by these powerful AI models.
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