Automating The Coding Process With Neural Networks

The use of neural networks is not new to data analytics. The fact that these can be used to automate the coding process and make it more efficient was first demonstrated by Kalyanmoy Deb in an article published on Here, we will discuss if this process can work and be implemented in practice.


We will see how we can use artificial intelligence (AI) and deep learning to generate thousands of lines of code for us.

What Are Neural Networks?



Before delving into the details of using neural networks for coding, let us look at what precisely neural networks are and how they work. A neural network (NN) is a collection of nodes connected to form a network. Each node performs a simple computation like addition or multiplication and passes the result to its neighboring nodes. In this manner, the final output is obtained after traversing through each node sequentially. The set of instructions used to perform this computation is called the 'network model.


Though NNs have been around since the 1950s, the widespread adoption of these models only began with the advent of Deep Learning models or Artificial Neural Networks (ANNs). ANNs can learn from massive amounts of training data without requiring any preprocessing step. They can also classify data with much higher accuracy than traditional statistical methods as they learn from historical patterns instead.


Neural networks are a type of artificial intelligence that attempts to mimic the brain's structure. They work by taking a particular input and then adjusting their connections to predict what the output should be. Neural networks are designed to be flexible to be used for a wide range of applications. The most common use of Neural Networks is in machine learning, where they allow computers to learn from data without being programmed explicitly.


Neural networks are composed of layers, each of which is a mathematical function applied to the previous layer's output. You start with an input image and pass it through the network to generate an image. The first layer will apply a series of transformations to the image (changing its colour or rotation, say). Each successive layer applies another transformation until eventually, you reach the final layer, where you get your result (the enhanced or transformed image). As you pass an image through a neural network, each layer does something different. Some layers might be identifying features in the image; others might be detecting edges or contours. Each layer learns its job by modifying its internal weights based on feedback from the previous layer.


The neural network has no idea what it's doing; it just follows its programming. But if you train it enough times with enough examples, it can learn how to identify specific features in images.


Neural Network Programming Tools



The most efficient NLP tool available is a neural network structure known as the transformer. In short, transformers utilize an encoder and decoder architecture which encodes words to an unintentional space and decodes it to the translation, typo correction or class (you might imagine an encoder-decoder as compressing a complex feature space to a more manageable space through neural networks (also known as a nonlinear function approximation). One of the most important tools within this NLP space is attention, which learns what words to pay attention to and the length of time (rather than hardcoding the system).

A transformer combines these tools with several other innovations that allow the models to be efficiently trained in parallel.


Neural Network & Code



Let's first look at the latest generation of programming tools and discover what they could accomplish.


The first are devices that are designed to detect bugs automatically. This is one of the most effective uses of neural networks in programming and is immensely useful to certain programmers. The Swiss-based company DeepCode led the way with this kind of tool; however, even their tool is not without its limitations.


There are also various programs designed to create basic code or automatically complete code for programmers. These tools are currently being made available to users of various development platforms. Facebook has created a system known as Aroma that can auto-complete small programs. DeepMind has created an artificial neural network that can create better versions of simpler algorithms that are not created by humans.


There is also the most thrilling use of neural networks in programming: the work carried out by a team of researchers from Intel, MIT, and the Georgia Institute of Technology. Researchers have developed a system known as Machine Inferred code Similarity MISIM. They claim they will determine what they call the "meaning" of a piece of code the same way that NLP systems can read an entire paragraph of text written by humans.


The MISIM system is expected to be a game-changer. Because it is language-independent, the system could read code as it is being written and automatically write modules to achieve everyday tasks. The majority of the code used in creating the cloud-based backup is identical across multiple programs, and the compliance processes can be a significant time-sink for many developers.


Systems such as MISIM can make writing code better than it currently is. However, they do have some significant shortcomings.




Coding software based on neural networks isn't likely to take over human coders soon. To better understand why we must look at the inherent weaknesses in the three principal ways these tools are used.


The first point is that ML or AI programs are created to spot bugs in software written by humans can be highly beneficial, however, only to a certain point. As you'll be conscious of using one of these programs--they are known to give an overwhelming number of false positives. Elements that the AI believes may be bugs yet aren't. This is because these programs are inclined to be cautious, of course, sound from a security perspective. Still, it's an indication of their lack of ability to comprehend the complexities of modern-day programming.


The second is that tools such as Aroma and OpenAI's GPT-3 model for language can create simple pieces of code using natural language descriptions, however, only under the guidance of humans. They do exceptionally well when presented with a restricted and controlled problem to address but do not seem (so far) to be capable of reviewing a design document and determining the most effective strategy.


The third kind of tool I've previously mentioned, namely MISIM and associated systems is, without doubt, the most creative application of neural networks in programming and has the highest potential to bring about a significant change in the way we function. But it must be noted that this tool is still in the beginning stage of development and is still far from being a fully-fledged beta.


It's worth noting that there's a fundamental issue inherent in each of these tools: the ability to think creatively.


Also, although these tools are excellent at writing code in response to the prompt, they're not likely to win any design awards, whether for design or coding. Even the top Web design application has been unable to use AI-driven aesthetic tools. There is a reason why human beings know what's appealing to others.


Utilizing this artistic, creative capability has been a vital aspect of the programming paradigms of the last few years. This is among the reasons, as an example, that most of the top front-end development frameworks currently available are visual-focused. Humans are adept at spotting patterns within seemingly disconnected data, and AIs excel at completing routine, time-consuming tasks. That is going to be the norm for quite some time.


Collaboration And Creativity


The inability to develop new solutions is why neural networks aren't likely to replace human beings. Instead, we must determine which tasks can be most easily accomplished by AIs or machines and which can be done best by humans. We can then develop an approach to code that draws upon each of the advantages.


There are several obvious options for making this happen. One possibility is to employ AI Software to teach human developers in a more adaptable, efficient, and focused manner than is currently available in our educational system. It is possible, for example, that automated recommendation systems can be used to teach security in programming for beginners by offering precise guidance on how to secure the systems in real life while they are written.


Additionally, AIs have shown incredible potential in monitoring the actions of human coders and improving their efficiency. An excellent example is the automated processes that numerous companies are using that use an ML-based system to monitor employees' actions. Providing every individual coder an AI assistant who learns about how they function and makes suggestions in light of their previous experiences is a massive advantage to most developers.


Thirdly, tools like MISIM, even though they're not able to completely automate the coding, could have an unexpected advantage: they could be used to write new code for older systems. Tools like MISIM are platform-independent, which means they could teach themselves to understand ancient coding languages like COBOL and then re-write these programs in a usable format like Python.


These approaches do not aim for the replacement of human developers by machines. They're all based on a distinct concept: when it comes to coding, humans can collaborate as colleagues rather than as rivals.


Neural Networks Examples


Think about a system seeking to cut down on online membership churn (ended subscriptions). The company could create a model to decrease the churn rate on the current batch of vulnerable customers (e.g., users who don't often click in the replay buffer of the algorithm - to come back later); however, this may be at the expense of people who have not been intended to join until the model that follows is to be re-trained.


This step could occur in the form of parameters that alter the text of emails to people on an email list. The rule (the model that determines the actions) could change the content for each email address registered that targets a range of preferences for email, e.g. people who love emoticons (many individuals do). Because there isn't a gratuity in an improvement, adding emojis is not everyone's preferred choice and could symbolize a reward that the engineer receives when the new model has been deployed. This modification could be performed in an automated way. However, this change may result in customers who are typically one of the loyal clients but then becoming angry, which could result in a negative feedback loop of churn, to speak.


Another example is with suggestions. I watch many hours on YouTube and have observed my interests change with time on the web and within the physical world and on YouTube specifically. YouTube is an excellent platform for learning about the world. YouTube algorithm is already showing the behavior of reinforcement (aside from the controversy over how widespread their algorithm is to tune the content they offer). The basic behavior is that I click on a subject I had not seen in a while or for years in the next day, and the number of posts I receive related to that topic will increase dramatically. I believe that Google's algorithm includes a highly weighted historical term or permits recommendations engine parameters to alter quickly. This feedback is not dependent on my interest's development outside of YouTube and should be thought about more thoroughly.


The churn and recommendation algorithm, in this case, demonstrates an understanding of feedback, an effect in which your future state and dynamic change in time, and based on your current behavior and condition.  Reinforcement Learning (RL) is the area of machine learning built around a world in which the only information we have about how we can improve an activity is the noise returned to us by the world. This representation usually takes the form of the use of a reward function. Still, it could be applied to any of the signals sent back to the programs that we regularly utilize to let them know how they are changing our actions.

Bottom Line


It may sound like a utopian vision; however, take a more comprehensive perspective, and you'll find the idea is possible. The emergence of AI and ML tools for code mirrors the creation of graphical coding tools and even programming languages in themselves. Your front-end development tools or Python scripts communicate with your hardware at a fundamental level. Everything is, let's not forget, must be converted to machine code binary.


Coding as a method that involves "translation" might be out of style; however, it is a method that has undoubtedly influenced programming training for many. A few decades ago, we were instructed explicitly on how our code was converted into assembly language. Today, this learning method would appear to be an unnecessary waste of time.


In the end, this is what the advancement of AI programming tools aims to do. The ultimate goal is for systems such as MISIM to analyze an outline of a computer program or even the description of a problem that needs to be solved and create software by themselves. However, it is essential to keep in mind that a human will have to take over one way or another.

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