Access to development talent and the growing number of tasks necessary to produce software are major barriers to software’s purported “world-eating” ambitions. There is a huge gap between the demand for software developers and the supply of qualified people entering the labor market. The majority of a programmer’s time is still spent on maintenance tasks such as testing, security fixes, code reviews, and bug fixes, even for those who are currently employed in the field. Recent advancements in AI-driven natural language processing (NLP) models are making it a reality to increase worker productivity in light of these two trends. The newest generation of state-of-the-art NLP models, known as generative pre-trained transformers (GPTs), can translate across numerous languages, including from text to code, thanks to its size, underlying architecture, training data, and training regime. Incorporating this potent capability into developer tools is proving beneficial in improving developer efficiency and removing technical barriers to software development.
Software Struggles to Eat the World
Jobs requiring coding expertise have grown in tandem with the proliferation of software for a wide variety of use cases and corporate applications during the past decade and a half. Unfortunately, many of these vacant programming positions remain unfilled, as a 2017 survey by Code.org found that there were an estimated 500,000 such openings in the United States alone. However, only 43,000 computer science graduates enter the market annually, a number that has been declining in recent years. Furthermore, it typically takes between three and five years to get qualified for these positions; by the time a programmer is ready to assume a more senior capacity, the number of positions will have grown by 28%. (U.S. Bureau of Labor Statistics). As a result, many businesses struggle to create game-changing software due to a lack of available talent.
Even after an in-house development team has been established, the organization will continue to face obstacles. There is a growing complexity in the quality, security, and timeliness requirements for software development. Surprisingly, developers only devote 30–40% of their effort on implementing new features or enhancing current code. (Newstack). This is due to the fact that a significant portion of their time is spent testing new features, implementing fixes, and addressing security concerns. The senior developers on a team will spend some of their time reviewing code and mentoring the junior devs. All of these factors can have an effect on how quickly and cheaply a software project is delivered. It’s also a great chance to team up AI and human engineers to fix some of the issues that have been plaguing the field.
AI + Developers = Pair Programming Dream Team
AI-driven Coder Tools
Recent advances in deep learning-based natural language processing have led to the development of a variety of AI-powered tools aimed at boosting developers’ efficiency and code quality. In particular, these tools’ underlying models can scan code to spot errors and vulnerabilities, automating some of the review process’ more onerous tasks. CodeGuru and DeepCode are two examples of recently published programs that do this function; they discovered vulnerabilities that are difficult for humans to spot and found that 50% of the pull requests they analyzed were flawed. (AI-News).
In addition to assisting with auto-completion of code, monitoring code output for errors, and even auto-generating unit tests, cutting-edge NLP approaches can enhance code quality and accelerate development. This can be done with remarkable precision using OpenAI’s Codex algorithm, which was recently included into GitHub’s Copilot. (Figure 1). The training data, which included both code and snippets of natural language, is responsible for this skill. According to an early study conducted by GitHub, using AI in tandem with a developer can cut the time needed to do a routine activity like creating an HTTP server in half. The fact that the underlying concept of this tool can auto-complete entire portions of code from a single comment also greatly simplifies programming for novices and non-technical users.
Coding Made More Accessible
Within reasonable bounds, anyone can use natural language to generate the necessary code snippets thanks to the excellent AI translation capabilities of the latest generation of NLP models. These programs can be written in any language, including those used for database access (SQL and NoSQL) or for executing specific routines (Python, JavaScript, or C++). Codex and similar programs are useful if the software one wishes to write is not too complex. It has been put to good use in the creation of basic websites, the derivation of Excel functions, and the translation of user intent into database query language. Researchers of these methods acknowledge, however, that the models are not always spot on. The code that is generated is usually basically correct but needs tweaking by a human programmer. In this way, the models can improve the efficiency of a human coding tutor by taking over when the AI encounters problems while being employed by someone with less skill. This can also mean that junior developers are far more productive while needing less guidance from more experienced colleagues.
From Human Language to Computer Language
So, how exactly do most of these gadgets function? The GPT is the workhorse behind these cutting-edge instruments, often a GPT-3 (or third-generation) type. OpenAI pioneered this framework, which was then trained on a huge corpus of online material including prose, e-books, tweets, code repositories, user comments, and news stories. Codex was ultimately developed as a byproduct of the model, whose primary purpose was to improve the realism of language generation.
Generations of GPT differ from earlier deep learning-based NLP models in several key respects. These include the number of parameters the models have and their state-of-the-art underlying architecture, the amount of data required for training, and the training methods employed. For the same reason that larger models may learn more nuanced patterns than smaller ones, the number of parameters in these and related models affects the intricacy of relationships they can capture in data. These models were also trained in an unsupervised, multitasking environment. AlphaGo, which excels at Go but not chess, is a good example of a neural network designed to execute a single task and hence requiring specifically-labeled data to learn how to accomplish that activity. Supervised learning is the type of learning that requires labeled data. In contrast, the GPT-3 was trained to predict the next word in a sequence; this forms the backbone of numerous tasks like translation, text synthesis, and question answering, and so does not require labeling of the input.
In addition, there exist transformer models, which surpass the efficiency of long short-term memory models and recurrent neural networks, which were previously used as NLP standards. Sentences are processed as a whole rather than individually, attention is used to keep track of how similar two words are, and information about where in the phrase a token occurs is encoded. (Figure 2). Since the problems with parallelization and recursion are now solved, a larger, more capable model can be built. It also solves the problem of earlier models’ forgetting the connections between words and sentences that are separated by a great deal of text. The GPT-3 may be integrated into other AI for coding solutions thanks to OpenAI’s application programming interface, which expands the reach of these tools even further.
Conclusion
Artificial intelligence (AI) is stepping in to aid enhance developer productivity so that a massive amount of software can be built to cover all the applications that demand it. Since there is a severe shortage of qualified programmers, businesses are increasingly finding it helpful to employ alternative methods of increasing both the quantity and quality of the code produced by their existing staff of programmers. The goal of an AI-powered pair programmer for human coders is becoming a reality with the recent remarkable breakthroughs in AI-based NLP models, such as the notably powerful GPT-3. Having these models included into developers’ regular tools has many potential benefits, including less time spent on mundane activities like creating tests and higher quality code thanks to automated reviews and pre-made snippets. The text-to-code capabilities that are now accessible are useful for everyone, from inexperienced coders to non-technical users. Even if software can’t take over the world by itself, artificial intelligence can help make it happen.