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Benefits & Challenges of Software Development with Machine Learning

Software development with machine learning involves using ML algorithms and techniques to build software applications. These applications can range from simple data analysis and prediction tools to more complex systems such as image recognition, natural language processing, and autonomous systems. The process of developing such software typically involves the collection and cleaning of data, selecting and training models, evaluating performance, and deploying the final product.

 

Several benefits are associated with software development, such as:

 

Automation and improved efficiency: ML models can automate tasks that would be time-consuming or difficult for humans to perform, such as image recognition or natural language processing. This can lead to improved efficiency and cost savings.

Increased accuracy: ml models can achieve higher levels of accuracy than traditional software in tasks such as prediction and classification.

Handling big data: ML models can handle and process large amounts of data, making it possible to extract insights and identify patterns that would be difficult or impossible to detect manually.

Personalization: ML models can be trained on individual user data, making it possible to personalize recommendations and experiences.

Real-time decision-making: With the development of edge computing, ML models can make decisions in real time, enabling the development of applications such as autonomous vehicles, robots, and IoT devices.

Innovation: Using ML models and techniques opens doors for new possibilities, which can lead to the development of new products, services, and business models.

Overall, software development with ML offers the potential for significant advancements in automation, accuracy, and efficiency in a wide range of industries and applications.

 

But, while there are multiple benefits of software development with ML, there are also some challenges that may arise:

Data availability and quality: ML models require a large amount of high-quality data to train and test on. If data is not available or is of poor quality, this can make it difficult to develop accurate models.

Model selection and tuning: There are many different ML algorithms and models to choose from, and selecting the right one for a given task can be challenging. Additionally, fine-tuning the parameters of a model to achieve optimal performance is a time-consuming process.

Overfitting: Overfitting occurs when a model is trained too well on the training data and does not perform well on new, unseen data. This can be a common problem and can be addressed using techniques such as cross-validation and regularization.

Explainability: Some ML models, such as deep neural networks, can be difficult to interpret and understand. This can make it challenging to explain how a model is making its predictions and to identify any potential biases in the data.

Deployment and maintenance: Deploying ML models in production environments can be complex and requires specialized knowledge. Additionally, these models need to be continuously updated and maintained as the data and requirements change over time.

Ethical concerns: There are many ethical concerns that arise when using ML such as bias, transparency, and accountability. It’s important to consider these concerns when developing and deploying such models.

 

ML is becoming increasingly popular in many industries and is expected to have a significant impact on the economy in the near future. In general, ML and AI are considered to be one of the most promising fields in technology and key driver of digital transformation and innovation. As companies are investing in this technology to improve their products and services, automate tasks, and gain a competitive edge. And this is across different industries such as healthcare, finance, retail, logistics, and manufacturing.

 

According to a report by the Belgian government, the AI market in Belgium is expected to grow rapidly in the next few years, with the government investing heavily in research and development in this field. VLAIC, aka AI research center Vlaamse AI-coalition, is an initiative from the Flemish Government to support the development and use of Artificial Intelligence (AI) in Flanders, Belgium. The goal is to make Flanders a leading region in AI by 2025.

AI: The next step in Software Development

AI has been revolutionizing businesses worldwide, from healthcare to banking, from automobiles to logistics. It’s innovations are developing very quickly and growing significantly on a global scale. AI refers to technologies that make it possible to equip computer systems based on algorithms with human abilities such as thinking, learning, problem-solving, etc., to make them intelligent and thereby help people to carry out different tasks.

 

With advances in machine learning, natural language processing, and data analysis, also in the world of software development, technology is changing rapidly and AI is leading the way. The global AI market reached USD 93.5 billion in 2021 and will expand at a growth rate of 38.1% annually by 2030. Innovations such as Edge AI, computer vision, decision intelligence (DI) and machine learning (ML) are shaping the market in the years to come. Additionally, robots are increasingly penetrating our everyday lives. And the current research suggests that this trend will continue in the coming years when robots and drones can take on more and more tasks in a meaningful way. These advances are related to the popularity and widespread use of AI & its promises of impressive growth opportunities.

 

The aim of AI is to create machines that can work and react like humans. However, AI is not just about creating human-like machines; it is also about making machines that can work better than humans. For example, a machine is able to process data much faster than a human can & also remember more information than a human can.

 

Since AI offers great potential for different areas, it is no wonder that its use cases are becoming more diverse with each passing year. AI solutions are already helping with:

  • Business process automation
  • Automated document creation
  • Management of production processes
  • Predictive Maintenance
  • Customer Analytics
  • Risk management
  • Supply chain management
  • Personalized service delivery
  • Software development

 

Since its inception, AI has made significant progress in software development. Early successes included creating programs that could play checkers and chess, as well as solve simple mathematical problems. In recent years, AI has been used to develop more complex applications such as autonomous vehicles, facial recognition systems, and machine translation. Looking to the future, AI will continue to play an important role in software development. With the rapid advancements being made in machine learning and natural language processing, there is no limit to what AI can achieve. As we move forward into the next era of computing, it is exciting to think about all the new possibilities that AI will enable us to realize. Let’s explore how AI is impacting software development and how it will continue to revolutionize the industry in the years ahead.

AI Software Development

Software development aka application development consists of winding together instructions for one or more programs that carry out required tasks or actions. The development team carries the task of translating problem-solving processes & algorithms into program code. Basically, we’ve known the classic methods of software development such as the agile and waterfall methodology. However, AI development works differently than classic software development. In AI development, data plays a central role – it is the center. In AI software development the behavior of AI depends on the self-training with the data. In the classic approach, the programmer had to set the rules himself, something that isn’t possible with AI development.

 

With AI, developers can create smarter and faster algorithms that can more accurately comprehend our intentions and behaviors within their applications. AI platforms promise faster development, more accurate prediction of user needs and behaviors, and continuously improving algorithms for data processing. This helps developers automate various tasks, from code quality analysis to bug fixing & save time on repetitive tasks. For example, if a developer needs to fix a bug that occurs often, they can train an AI system to automatically detect and fix that bug. This frees up the developer’s time so they can focus on more important tasks.

 

AI can also improve the quality of code. By using AI-powered static code analysis tools, developers can identify potential errors and bugs before they even write any code. This not only saves time and money by preventing buggy code from being deployed, but it also helps improve the overall quality of the software.

 

AI can help developers create more user-friendly applications. By using machine learning algorithms, developers can automatically generate user interface (UI) designs that are optimized for conversion and usability. This means that users are more likely to have a positive experience with the application, which could lead to increased customer retention and loyalty.

 

The work & results in AI development are characterized by the acquisition, analysis, and preparation of data and by training the models. We can say that an AI solution is gradually approached through smaller experiments and experience is gained in the process. For this reason, exact, conscientious, and transparent documentation of every single step & every attempt are essential.

 

To achieve quicker results, however, several work streams can run in parallel, all of which are dedicated to solving the same topic. This requires a high degree of flexibility.  It is evident that the rise of AI will revolutionize software development and open up a world of new possibilities. With its ability to process information faster than ever before, AI technology can help streamline projects and shorten production times. As more companies continue to invest in this form of technology, we can expect even greater advancements from artificial intelligence in the future. There’s no doubt that there are both positive and negative implications associated with embracing this kind of technology but for now, we must use what advantages it offers us to move forward into the digital age.

Test automation: Getting more control and business value

During the crisis, many companies created ad hoc online services to help their employees and customers. These services were available to the public within a few weeks. However, we know that the more pressure that a company is put on, the more likely they are to make mistakes. Online services can suffer from various factors, such as the lack of trust in their applications and the end-user’s dissatisfaction. In the long run, this can lead to lost sales and customers, not to mention the company’s reputation.  This is why it is important that companies have the proper quality assurance (QA)  procedures in plac, before, during and after the digital initiatives. One of the most important factors that the crisis highlighted was the need for continuous testing.

 

Today, the majority of businesses use agile development approaches and DevOps practices, often with at least daily builds. On the other hand, testing is still mostly done manually, which slows down the quick deployment of software. Because conventional testing cycles usually last weeks and can no longer keep up with the ever-shorter release frequencies that digitization brings with it. But development and DevOps teams need continuous feedback throughout the release and digitization cycle to ensure the quality of their work. Doing this with manual tests is simply impossible. However, when organizations don’t continually review how the latest code changes impact critical end-to-end transactions and the user experience suffers. To solve the dilemma, test processes must also become agile and integrate seamlessly into the continuous delivery pipeline. A development towards continuous testing becomes indispensable. Test automation delivers value when it is performed frequently. But it is precisely then that it must be robust in order to deliver reliable results. This method can help companies deliver new applications faster and improve their overall performance.

TEST AUTOMATION

Companies must ensure that their applications and processes will still function smoothly in any given scenario. Careful QA and BA is extremely crucial, especially in the business-critical SAP environment. As business processes often span a complex network of SAP and non-SAP applications. Countless components interact with each other. This makes quality assurance very complex. You have to consider the entire process chain across all links. First of all, this requires precise knowledge of all dependencies. An automation platform can significantly reduce time and costs, and enable faster, more secure application delivery. With the aid of artificial intelligence (AI), it can automatically run end-to-end tests, identify exactly what needs to be checked, and analyze risks.

 

Teams responsible of QA and BA remain under pressure even after the full implementation of the digitization strategy. Because digital landscape keeps changing constantly. Businesses face challenges in continuously improving their offering. On the one hand, they let their customers enjoy new products/services and innovations more quickly. On the other hand, they must also thoroughly test every modification along the entire process chain. This explodes the QA/BA effort.

 

Here is where traditional manual test procedures hit their breaking point. They are excessively labor-intensive, slow, and expensive. Therefore, businesses are adapting a new approach to effectively and automatically include tests into release procedures. A platform for automation enables this. A Forrester study claims that with their assistance, QA/BA teams are able to quickly test numerous updates and pieces of software each day.

 

Organizations are unable to afford labor-intensive, manual QA/BA processes in any digital process. It is too likely that errors will be made and crucial business operations and users  inside and outside the company would suffer as a result. Customers can also get irritated and switch to the competition if an application or website no longer functions as usual. Preventing errors in digital business processes is also crucial for productivity and employee satisfaction. Because program crashes, poor application performance or other problems cost valuable working time and demotivate users.

 

For a company’s succcess, it’s required to make the transition to the digital world but doing so also calls for very effective QA/BA procedures. This is not possible without test automation. Working with a specialized service provider is advised in order to introduce an appropriate solution. Businesses must change and adopt a new quality assurance strategy so the test automation can be taken into consideration from the beginning of a digital transformation project.

Software Development: Why is Software Quality Control + Testing So Important?

Software development is constantly changing and becoming more and more complex. In order to keep up with this development, the ever-changing and innovative business landscape, test tools, and security requirements were further developed. Development teams are under increasing pressure not only to create quality software on a tight schedule but also to ensure that the software complies with both internal and external standards such as GDPR.

 

This also means that, as far as we know today, there are no error-free applications. Problems can arise in software development that has consequences: There is a risk that the originally planned development costs will be exceeded, leading to a delay in the development period. There is also the risk that the end product will have qualitative defects.

 

This makes quality control so important. Testing software is, without question, very important for a high-quality end product. In order to make sure the released software is safe and functions as expected, the concept of software quality was introduced. Software Quality Testing is an honest review and evaluation of software regarding its quality and compliance with the organizations/client specified requirements, expectations, and market standards. The goal is to identify and fix errors at an early stage. Software testing is not only an important part of quality assurance but also an integral part of the software development process.

In this article, we’re going to look at the major challenges encountered during software developments and how can we resolve them to deliver a quality project.

 

  • Programming standards

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Nobody wants to take the time to plan but start programming right away. It is best for the programmers to sit down, discuss important points, such as choosing the right technology, interpret customer’s needs to properly defining the purpose of the software or even write the first few lines of code together. Because if every developer interprets programming standards in his own way, there can be significant discrepancies in the end results, which means that extensive reworking is necessary. It is advisable to provide internal manuals with the programming standards and, above all, to ensure that these are actually used by the project team. This is how each party, programmer, client, service provider, etc. can save time and be productive right from the start.

 

  • Ensure clear processes

Due to a lack of planning, the exact requirements for a project are also missing. Non-transparent and inaccurate description of the processes in software development can quickly lead to uncertainty among those involved. For example, in many projects, the requirements are only formulated in writing and are not communicated correctly. This is mostly due to the lack of communication between project leaders, team leads, and managers and between the actual project formulator and the one who programs the whole thing. This makes it extremely difficult for the developer to implement the right things.

Misunderstandings can result in huge problems as a result of which errors creep in, which can only be eliminated again with increased effort and the project can be unnecessarily long. It is recommended to enable direct communication between the project formulator and the programmer.

 

  • Set priorities

It is noticeable again and again that no priorities are set with regard to the planned features, which usually means that the features are completed in the wrong order and or errors are often made, especially when it comes to the selections of technology for development. New, exciting technologies are often used but the problem arises when the manufacturer of the new technology decides on an update, then you may have to rewrite the entire code. A thing that is very time-consuming and not really practical. Additionally, only prioritized tasks should remain in the project scope.

It is crucial, but it can be a challenge to prioritize several dozens of items efficiently. A dedicated team should therefore decide in advance which work step is to be tackled first with already tried and tested technologies. It is advisable to make the decision internally and to leave the client outside. Because clients often lack an overview of which feature is particularly important for the success of the project. With prioritization, you can also keep the track of the expenses and effort do your team spends on the project activities.

 

  • Project’s delivery time

There is hardly a single project in which the timeframe of software development is respected because the time required is incorrectly estimated at the beginning of the project. Due to the new requirements made by the client during the project, but also to the fact that a developer cannot accurately assess larger projects can cause delays in project delivery.

Delivering software on time is never easy when the scope and timelines in question are significant.  To deliver software projects on time, it’s recommended to break down the assignment into smaller modules of a few days. This makes it possible to give a more precise expectation of what will be delivered and when it will be delivered.

 

  • Professional qualifications of the employees

For every project, it should be ensured that all employees involved are adequately qualified. This is especially true for very complex software projects in which many developers are involved. It is also important that the individual employees have a similar technical level in order to avoid communication problems with their team members.

Additionally, it should be avoided that the developers assigned for a project leave the company while the project is running and are replaced by other/new employees. Because if an employee leaves the company during development or changes to another project, it is extremely difficult to transfer the knowledge of a departing employee to the new developer.

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