How to Use AI-Powered Coding

Programmers are going to have some fun with this one. Picture the start of a typical day as a developer, all the priorities you need to set, preparation for any briefs you’ll get, and then the contingency planning on top of it all. Of course, nowadays with tools like Chat GPT, Visual Studio IntelliCode, and CodeSpace among other resources, the programming process has improved lightyears from what we had 10 years ago. 

With that in mind, let me ask you this (if you are a programmer); how much easier is it to approach a day on the job now compared to when you started programming? Better yet, how much easier would it have been for you to learn to code with the tools available today?

Answers to these questions will vary. A lot of software developers will argue that you need to learn the principles of programming without the “shortcuts” first and then introduce them as you get better. Others will argue that modern programming tools have made it much easier when learning to code and made the process more enjoyable. There really is no right or wrong answer to these questions because it comes down to one thing; these tools are the future. 

The Basics of AI-Powered Coding

AI advancements in the software development space will only get better and make the process more streamlined. Among the many tools programmers are leveraging in 2023, AI-powered code is a top choice to enhance the efficiency and effectiveness of their coding process. 

For the first example, we’re going to be looking at OpenAI’s Codex. Codex is an AI code assembler that uses machine learning to translate natural language into code. Here are 3 examples of using it: 

1. You’re creating a feature that tracks sample inventory in a LIMS system:

You could say to Codex: “Develop a functionality that can record and track the inventory of samples in the laboratory”

Codex will then: 

• Design a plan for a database that will store the following information: sample ID, sample type, quantity, and location.

• Implement CRUD (Create, Read, Update, Delete) operations for managing the sample inventory.

• Develop a user interface that allows users to see and update sample inventory records as they go.

2. You’re creating a feature for insurance software that calculates premiums based on risk factors: 

You could say: “Build a component that calculates insurance premiums for different insurance policies based on factors such as age, location, and coverage type.”

Codex will then:

• Design a data model that can store policy details, consisting of customer information and risk factors.

• Develop algorithms that calculate insurance premiums based on the risk factors provided and the parameters of the given policy.

• Lastly, implement validation checks to ensure the premium calculations are appropriate.

3. Now you want a feature that automatically tracks budgeting and expenses for a FinTech platform:

You could say: “Create an interface that users can use to set budgets, track expenses, and generate reports on overall spending”

Codex will then: 

• Create the design for the interface where users can input budget categories, set spending limits, and track expenses.

• Develop algorithms that will categorize expenses based on predefined rules and transaction data automatically.

• Generate visual reports that users can use to understand their spending habits, see how well they adhered to their budget, and recognize trends in their expenses.

Codex is a great platform, but it’s not the only one. Platforms like Tabnine, Snyk, and IntelliCode all perform unique roles that help software developers perform at the next level— but the key is knowing how and when to utilize them.

What Tools to Use and When

As a software developer, your time down to the second is highly valuable, so anything that can streamline repetitive processes or reduce manual effort is a go. Software depends on context which means that the tools a developer uses will depend on the specific requirements of the project and the developer's preferences.

With that in mind, here are some ways the AI-powered coding tools we’ve mentioned can be used in different situations:

1. Project: You’re building a mobile app for a transportation company

Tabnine will be a great resource in this case to handle the following: 

• The coding process for the mobile app will be shortened thanks to Tabnine’s code completion ability.

• The system can give suggestions for implementing features such as map integration, user location tracking, and in-app notifications. 

• It will get the right APIs involved so that third-party transportation services can be integrated.

  2. Project: You need to design and build a Web App for an E-Commerce store

The Web App development process can become a lot more satisfying using IntelliCode because of the following: 

• Whether it’s HTML, JavaScript, CSS, Angular, React, Vue.js, or you name the web app development language or framework - IntelliCode can suggest relevant code completions based on the context using Deep Learning algorithms.

• Because IntelliCode is so great at detecting what is necessary to complete a code, its error-prevention ability is significant

  3. Project: Develop a Node.js-based REST API

Snyk will be great for the security aspect of your REST API. 

• It identifies any known vulnerabilities in the open-source libraries you're using with your API and then provides insights and actionable recommendations to mitigate these vulnerabilities.

• The good news is that this is not a one-and-done tool. Snyk can continue to monitor the system over time and keep identifying gaps to fill.

Why AI-Powered Code?

The advantages that come with properly utilizing AI coding tools are immeasurable. Software developers are performing at a new level in today’s world thanks to these advancements. This means that now is the time for a business to get on before you start getting dramatically outperformed. 

AI-Powered code relies on 5 key components which include; the data source, Natural Language Processing (NLP), code parsing, Machine Learning, and most importantly feedback and iteration from the software developer. The interaction aspect is what allows the AI models to refine their predictions and generate accurate, or more contextually appropriate coding suggestions. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

5 of The Best Debugging Techniques

Your application, platform, or website relies on error-free code to ensure optimal performance and to provide an overall higher-quality end-user experience. If you neglect this, bugs will wreak havoc on your product and turn users off from it altogether. Though quality assurance testing is common practice during software development, developers still play a vital role in the debugging process. 

Debugging techniques vary by language and tool, yet there are many straightforward methods for developers when approaching the debugging process that can be applied to almost any software. In this article, we'll explore some of the most practical debugging techniques that programmers can use to enhance their software simply and effectively. In addition to this, we’ll look at 5 specific debuggers and what exactly they’re good for. 

How Can I Debug Easily?

When a programmer begins debugging, they are simply identifying and removing any defective code within the program. Although finding bugs is a crucial element of QA testing, it’s still just one part of a broader phase that entails verifying other aspects of quality, including usability and overall performance.

The reality of debugging “easily” is to think about the problem less from a technical aspect and more from a systematic approach. With that being said, here are 5 ways to get started:

1) Break The Issue Down Into Simple Questions:

The first step to debugging effectively is to define the issue at hand and ask yourself the simple yet important questions related to it. For example:

• What is the desired outcome of your program?

• How is your program currently functioning?

• What errors are you encountering?

• What past experiences have you had with similar problems? And what solutions from then may apply now?

• How many possible causes and locations of the bugs can you list?

Questions of this nature will lead the developer to form a theory about where to begin debugging and how to approach it logically.

2) How Do I Deal With Error Messages?

Though there is no feeling more gut-wrenching for a software developer than seeing an error message appear for the 3rd time, it’s the only way for them to realize what’s going wrong. So with that said, anytime an error message does appear, make sure you write down what’s wrong. By the same token, you can give it to Chat GPT, Visual Studio IntelliCode, or CodeSpace to get suggestions on how to solve the issue.

The fact remains that developers cannot ignore error messages or just hand them over to an AI to solve them. Getting suggestions from resources is a great first step and can speed up the debugging process - but attention to detail during software development is essential. 

3) Keep a Log

Branching off the previous point regarding writing down error messages, the key benefit of this method is that it’s great at identifying patterns/recurring issues. For example, take a developer who is integrating a REST API for a weather app but keeps getting the error message "404 Not Found" when trying to access the API endpoint to retrieve current weather data. 

The cause of this issue could be an incorrect endpoint URL or the API is simply unavailable or down. Either way, documenting the error and its details will make it easier for the developer to identify and address the problem. For instance, verifying the endpoint URL or seeking support from the API provider could be the best options when trying to resolve this issue. By logging that, the developer will be prepared should the issue happen again.

4) What About Debuggers?

A debugger can aid in pinpointing and fixing bugs in your code. These tools are also referred to as “debugging mode" or “debugging tool”. To take full advantage of a debugger, developers need to execute programming within it. This allows software developers to monitor it in real time and observe errors as they occur. By pausing the program while it's running, developers can identify and investigate the issues that are causing errors and then analyze the code line by line.

Some good debuggers include:

Visual Studio Debugger: This is a powerful and comprehensive debugger that is included with the Visual Studio IDE (Integrated Development Environment). It supports a variety of programming languages, including C++, C#, and Python, and even includes features such as step-by-step debugging, breakpoints, and data visualization.

GDB: This is a command-line debugger that is widely used in the Linux and Unix world. It supports a wide range of programming languages, including C, C++, and Fortran, and provides features such as breakpoints, watchpoints, as well as remote debugging.

Xcode Debugger: This is the debugger that is included with Apple's Xcode IDE. It supports programming languages such as Objective-C and Swift and also provides features such as source-level debugging, stack traces, and breakpoints (just as the last two do as well).

Eclipse Debugger: This is a debugger that is included with the Eclipse IDE. It supports multiple programming languages, including Java, C++, and Python, and offers features such as conditional breakpoints, expression evaluation, and remote debugging.

PyCharm Debugger: This is a debugger that is included with the PyCharm IDE for Python. It provides features such as step-by-step debugging, variable inspection, and remote debugging.

5) How Can a Developer Approach Debugging Logically?

It’s not unheard of for software developers to run into “analysis paralysis” when encountering issues with their code. For this reason, localizing the problem (line-by-line removal of code until the issue is found) is a great tool. Though it tends to be a tedious process, it can be the most efficient and effective way to solve the problem in some cases. 

Software testing, detecting, and addressing bugs before they disrupt the application is the foundation for a “logical” approach to debugging. While the QA team is responsible for thoroughly checking the product, developers should always perform simple tests during the development phase, such as unit testing, which may involve testing individual parts of the code, or units. 

Whatever the case may be, having a toolkit of simple debugging techniques is essential for creating and refining software, which ultimately leads to better products. Now, depending on the specific issues and situations encountered, some techniques may just be more suitable than others. However, top professionals would recommend that developers familiarize themselves with all of them to have a variety of tools available.

Summarizing Everything

Debugging is an essential part of building a sustainable software architecture. There are a variety of tools available to software developers today that can make this process much more efficient and effective. Software developers without debugging techniques are like a mechanic without a wrench. No matter how experienced or well-trained a software developer is, they will always run into errors and issues in their code which will lead to an unreliable and unsustainable software system. In a rapidly evolving digital landscape, debugging is paramount to ensuring that software systems are functioning as intended, and to prevent any issues that should arise from becoming bigger problems.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

10 Machine Learning Tools For API Integration

API integration sets up web services to process data in ways that benefit the UI/UX of an application or platform. It does so by allowing developers to leverage external APIs and Machine Learning (ML) techniques to enhance the functionality and usability of the software system. If/when they are leveraged, the software system will respond extremely well.

When examining the role that Machine Learning plays in API development - the key area to focus on is analytics and how applications use this to speed up product development. Machine Learning consists of algorithms and models that, when leveraged during API development, create a high-functioning data management and automation system. Now, the key variable to keep in mind here is the quality and quantity of data that the system has access to.

Processing Data

API development and integration are common practices when building a platform's back end, however, Machine Learning is still on the rise in terms of adoption. With that in mind, here are some of the benefits you can expect when combining ML algorithms with APIs:

• API provides a clean and well-defined interface for analytics

• API integrates easily with any application with a simple cURL command

• API endpoint remains the same even if the algorithm or input data changes

• API checks data and requests at the door for anything not corresponding to the specification, resulting in an error

• API separates the iterative world of data science from the world of IT and software

• Algorithms need frequent updates, software needs to be stable, reliable, and robust

• Data scientists can focus on building models without worrying about infrastructure

Many businesses run into problems when managing their data which include anything from quality issues with the data itself to errors when processing large quantities of data. This is where API and ML integration can be the saving grace - when automating data management processes, it results in the following:

• Cleaner and more structured data 

• The ability to predict and detect anomalies

• Provides personalized recommendations

• Automates repetitive tasks

• Manages large volumes of data with ease

Tools That Integrate Machine Learning Techniques 

We’ve looked at a few of the tools on this list but seldom have we taken a closer look at how they assist Machine Learning integration. Here are 10 tools that are great for this process:

1. H2O.ai: A Machine Learning platform that enables programmers to build and use large-scale Machine Learning models.

2. RapidMiner: A platform that allows developers to build predictive models and perform data analysis using Machine Learning algorithms.

3. Google Cloud AI Platform: A cloud-based platform that provides developers with tools to build, train, and deploy Machine Learning models.

4. TensorFlow: A Google-developed open-source platform that enables Machine Learning model creation and training.

5. Scikit-learn: A Python package that offers various data mining and analysis features, including Machine Learning techniques.

6. Keras: An advanced neural network API created in Python that can be used alongside TensorFlow.

7. PyTorch: A neural network development and training library that is open-source and based on the Torch library.

8. Microsoft Azure Machine Learning Studio: A cloud-based platform that allows developers to build, train, and deploy Machine Learning models.

9. Amazon SageMaker: A fully-managed platform that provides developers with the tools to build, train, and deploy Machine Learning models.

10. IBM Watson Studio: A cloud-based platform that also allows developers to build, train, and deploy Machine Learning models using various tools and services.

The main priority in mind when software developers integrate Machine Learning techniques with APIs is the flexibility and ease of integration on the back end. What this means is that the platform will be highly scalable, able to handle various data formats, and easily integrate with different programming languages as well as existing software frameworks. 

Developers often experience the temptation to expose an endpoint of the API to the code of the ML model but this does incur risk. When you maintain the separation of your model's code from that of the infrastructure, it allows the application to function in a more secure, reliable, and scalable manner. Whereas exposing endpoints can lead to error responses to requests and downtime of the endpoint. 

Going Forward With MLaaS (Machine Learning as a Service)

APIs provide programmers with a clear interface that organizes analytics and the application utilizing them which in turn speeds up the product development process and allows ML models to be used and reused across a variety of applications. With this, companies can expect to see a lot of change in their industry as the gold standard today calls for a strong digital strategy. In order to get ahead, businesses need to remain aware and begin strategizing before taking action.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.