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How Generative-Language Models are Redefining Software Engineering
May 31, 2023

How Generative Language Models are Redefining Software Engineering

ChatGPT has woken up the world to the revolutionary potential of artificial intelligence (AI) and machine learning (ML), capturing global attention and unleashing a spark of creativity unheard of previously. Its ability to replicate human speech and decision-making has given everyone the first real inflection point in the widespread adoption of AI. Now, the true disruptive potential of the technology is visible to everyone, everywhere.  

In light of newly released language models such as AlphaCode and GitHub Copilot, generative language models have become a trending topic for governments, investors, technologists, and the public.  

As the name implies, generative AI creates or generates text, images, music, speech, code, or video. The foundation model concept and the machine-learning techniques used to implement it have improved over the last ten years. But what exactly is the foundation model?   

The foundation model is a collective term for large-scale models with billions of parameters. With the latest technological advancements, organizations can develop specialized text- and language-generative models by leveraging these foundation models. Generative language models, or large language models (LLMs), are a type of generative AI and a foundation model.  

With major technology companies such as Google and Microsoft introducing Bard and Bing, this space is improving with time.   

Does this mean the end of the road for software developers? Or can developers leverage generative language models to improve their productivity?   

This article explains everything you need to know about this AI technology, from the rise of generative language models to its growing applications in software engineering and its limitations.    

The Rise of Generative Language Models and AI  

study by Financial Times indicates that investments in generative AI in 2022 surpassed $2 billion. The Wall Street Journal estimated OpenAI’s valuation to be worth an astonishing $29 billion for a potential sale of some shares. This demonstrates the immense interest that businesses and investors have in generative language models and related AI technologies.   

Businesses are starting to explore the countless potential applications of generative language models as the world continues to embrace technology and automation. This kind of AI is poised to develop autonomous, self-sufficient digital-only businesses that can interact with people without human intervention.  

Now, imagine a situation where a software engineer has a deadline to finish a challenging project that requires writing hundreds of lines of code. When an engineer enters the specs into a generative language model, the model creates the complete code in a matter of minutes, leaving the engineer with only the task of reviewing it and making any necessary changes.  

Although it may sound like science fiction, generative language models are already creating a stir in several industries, and it’s only a matter of time before it becomes a uniform tool for software developers.  

Deepmind’s AlphaCode and GitHub Copilot are some AI competitors in generative language models.  While Copilot suggests code, AlphaCode stands apart by analyzing algorithms and generating complex code that not only aligns with their descriptions but also exhibits a high level of competitiveness, devoid of errors.  

On the other hand, multiple SaaS solutions, technology platforms, and service providers are embedding generative language model capabilities. Gigster, for instance, released ChatGPT integration support, and Equally AI introduced Flowy, a ChatGPT-based web accessibility platform.   

Similarly, more than 150 startups and growth-stage companies have emerged and are already integrating the capabilities of generative language models.   

Bill Cusick, Creative Director at Stability AI, considers generative language models “the foundation for the future of creativity.”   

The Fear  

Generative language models are viewed as a transformation altering how things were done in the past, and their positive impacts cannot be overstated. However, it is essential to remember that AI-based models have some not-so-positive effects, particularly in software development. 

The Fear of Being Replaced 

Generative language models have evoked fear throughout the software engineering community, with many developers concerned that machines may soon replace them.   

Amidst these uncertainties, the question arises: Should developers be concerned about generative language models (GLMs)? Should they start preparing for a career transition?  

The answer is NO; software developers need not worry about being replaced by GLMs in the immediate future. While GLMs represent a remarkable achievement in natural language processing and code generation, it is important to recognize their limitations and the unique skills that human developers bring.  

Liz Couture, Software Development Manager at Geisel Software, said:  

ChatGPT is not at a stage where individuals without software development knowledge can simply request it to generate flawless software every time. It typically necessitates developers to review its output, troubleshoot any issues, understand its processes, and integrate it with existing software. Until ChatGPT reaches a point where one can simply say, “Create an app that accomplishes this task,” and it produces fully functional software, it will not replace developers. Instead, it will serve as a tool that assists developers. Developers already have various tools to analyze, enhance, and eliminate complex issues and bugs from their code. ChatGPT and similar systems will likely become another addition to these tools, empowering developers to be more efficient, enabling them to build superior and more sophisticated software.”  

As smart as AI has become in answering questions and writing code, there will still be a need for software engineers and developers, says GitHub CEO Thomas Dohmke. 

You must understand that AI can carry out a specific set of tasks. However, this performance is restricted to the data provided to it. AI will need ongoing improvements, which will necessitate human intervention. Even then, there will be some complex activities that only humans can complete. 

Shanea Leven, Co-founder and CEO of CodeSeeacknowledges, “Engineering requires a lot that AI can’t replace, like context, making it almost impossible for AI to load into a single model, train that model, and incorporate the predictive capability of humans who understand what will be necessary in five years. AI will never be able to handle many big-picture judgments unique to various enterprises.”   

The Fear of Getting Fired 

The use of generative language models may present issues with intellectual property ownership.  

For instance, it raises concerns regarding who owns the generated code if an AI model generates it based on a pre-existing software product. Legal conflicts and challenges defending intellectual property rights may emerge from this.  

Such actions can have a detrimental impact on the developer’s career and can also end up getting fired. Developers need to recognize that they are jeopardizing their reputation, livelihood, and freedom.  

Bloomberg reported that Samsung banned using ChatGPT and other generative language models after a developer uploaded sensitive data to the system, resulting in the company firing the individual. 

However, to overcome the fear of getting fired, it’s crucial to communicate and collaborate with your team and management. Highlight the benefits of generative language models and how they can empower developers to deliver higher-quality software more efficiently. But it’s also essential to use these tools responsibly to protect an organization’s integrity and intellectual property. 

The Fear of Code Vulnerabilities  

Let’s focus on the unintentional damage that LLM-based tools can cause, specifically related to code vulnerabilities and errors.  

If we delivered 100% secure code, bug bounty programs wouldn’t exist, and we wouldn’t need databases like CVE/CWE.   

Can pair-programming tools like Copilot produce better and more secure code than human programmers? This may not always be the case. These tools might even introduce vulnerabilities that an experienced developer would avoid in certain situations.  

Since code generation models learn from a dataset of human-written code, they inevitably pick up some less-than-ideal coding practices from the history of programming. Moreover, these models cannot differentiate between good and bad coding practices.  

Recent research on the security of Copilot-generated code reveals that “Copilot is more susceptible to introducing some types of vulnerability than others and is more likely to generate vulnerable code in response to prompts that correspond to older vulnerabilities than newer ones.” 

We’re saying it once again — software development and generative language models are not intended to be substitutes for one another. They’re meant to complement each other. 

If generative language models are used responsibly and in collaboration with software development practices, they can bring significant value and efficiency to the development process. 

The State of Generative Language Models in Software Engineering  

Generative language models are set to transform and revolutionize the field of software engineering, representing the most significant shift in decades. This advanced technology may simplify tedious activities that have long bothered engineers, such as reading and iterating over Python files. Generative language models can produce code based on the context of the developer’s goal rather than relying on websites like Stack Overflow or endless Googling, thereby significantly speeding up the development process.  

Today, the same technology is used for various software development tasks, such as code review, refactoring, and test generation. Generative language models can even aid in the process of identifying bugs, errors, and crashes.   

Even though this technology is still nascent, developers can already use it to generate code components based on specific context cues, reducing the need to sift through irrelevant results. Developers can free up critical time by deploying generative AI to focus on more complex issues, increasing productivity and efficiency.  

Suresh Sambandam, the Chief Executive Officer (CEO) of Kissflowsays, “Just like low- and no-code will not completely replace traditional developers and software engineers, generative language models will offer useful tools that minimize repetitive tasks and speed up time to market for app development.”  

Leven further adds, “We’re going to see an enormous shift, not just in productivity but also in how we obtain our information faster. AI will provide developers the power to accelerate the repetitive decisions that engineers must make, such as generalized inquiries about a language.”  

In its recent report titled Big Ideas 2023, ARK Investment Management LLC predicted that Generative AI could lead to a 10x increase in coding productivity. Moreover, the output of software engineers and developers might witness a 10-fold increase thanks to AI-based coding assistants like Copilot.  

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Benefits of Generative Language Models in Software Engineering  

Natural language processing (NLP) activities like language translation, text summarization, and text synthesis are just the tip of the iceberg for generative language models.   

The many use cases of generative language models include new search engine architecturesexplaining complex algorithms, building customized therapy bots, and developing apps from scratch.  

Other benefits of generative language models in the field of software engineering include the following:  

1. Code Documentation & Understanding  

Maintaining thorough documentation is crucial for software projects, but it can be a time-consuming task.  

Generative language models can help developers by automatically generating code documentation, comments, and explanations. This saves time, improves code readability, and facilitates better collaboration among team members. 

2. Improved Code Quality  

AI models can uncover hidden insights and recommend optimizations that escape the eyes of even the most skilled developers by harnessing the power of enormous data sets and cutting-edge algorithms.   

The outcome is code that’s sleeker, efficient, and easier to maintain, significantly reducing the likelihood of errors and bugs. Also, generative language models can identify and highlight potential security vulnerabilities, allowing developers to fix the problems and avoid expensive security breaches proactively.   

Developers can also translate code from one language to another, allowing them to conduct legacy migration and develop use cases.  With generative language models, software development has entered a new era of unprecedented brilliance and dependability.  

3. Achieving Higher User Expectations  

Users’ expectations for software products have increased with technological advancements, creating a tremendous challenge for developers. Generative language models can provide the unprecedented innovation and creativity needed to meet these high user expectations.   

These AI-based models can evaluate huge volumes of user data and discover major patterns and trends, giving developers crucial insights into user behavior and preferences, allowing them to customize their products to meet the demands of their target market.   

Moreover, language models can help with user interface design and user experience optimization, ensuring that products are highly functional and straightforward to understand and use.   

4. Automated Design Prototypes  

What if AI could support the development of new design prototypes? Generative language models transform how products are innovated, built, and manufactured. It can produce fresh ideas that would be complicated for people to think independently.   

Automatic data analysis techniques can be used to find patterns in consumer behavior and preferences that can be used to improve product design. As a result, it could improve the speed and precision of product design and prototype development.   

Moreover, generative language models can be used to automate the process of creating design prototypes. By generating natural language descriptions of design concepts, these models can help developers quickly create and iterate on prototypes without requiring extensive design expertise.  

For example, Uizard is a design and prototyping tool powered by computer vision (CV) and machine learning capabilities. It can help teams build interactive user interfaces (UI) with little to no effort efficiently.  

5. Natural Language Interfaces 

Generative language models have the potential to bridge the gap between software applications and human users.  

By enabling natural language interfaces, developers can create intuitive user experiences that allow users to interact with software using conversational language.  

This opens opportunities for building chatbots, virtual assistants, or voice-controlled applications, enhancing user engagement and accessibility. 

Collaborate with Trusted Software Engineering Partners  

Although generative language models have proven beneficial and have shown great potential, it is only optimal to rely partially on them for the success of your software development projects.   

Working with a competent, trustworthy partner who can provide the expertise, support, and personalized approach required to guarantee your software development projects are successful is considerably more impressive.  

Collaborating with a human team ultimately provides reliability and confidence that cannot be matched by depending just on generative language models.  

To learn more about how our skilled software developers can assist you in accomplishing your software development projects, request a demo and contact us! 

The Way Ahead  

This is a pivotal time. While generative language models have the potential to revolutionize the software engineering industry, it is crucial to analyze any potential challenges carefully.   

To ensure a diversified and resilient workforce within the industry, balancing the advantages of generative language models and the need for continual learning and growth of programming abilities is critical. 

About the Author

Dharnendra Shah

Dharnendra joined TechBlocks as a Sr. Technical Leader in June 2014 and has grown through the ranks to serve as Software Development Manager in the current capacity. At TechBlocks, he leads the software development team and oversees the design and deployment of complex software systems. Under his leadership, TechBlocks successfully deployed multiple software applications that helped drive the company's growth.

Throughout his career, Dharnendra has earned several certifications, including MCPD: SharePoint Developer 2010, MCTS: SharePoint 2010, Application Development, and MCTS: SharePoint 2012, Application Development. His cloud engineering and technical consulting expertise have helped him become a respected authority in the software engineering industry. He is committed to excellence and continually seeks opportunities to improve his skills and knowledge. He holds a Bachelor's in Information Technology from VVP Engineering College in Rajkot, Gujarat.

Dhanendra