Author: Devin Partida

Many government bodies have historically been averse to open-source software (OSS). Now that OSS has gained popularity and shown what it can do in the private sector, that’s changing. The open-source movement holds significant potential for public agencies, too, especially in the realm of finances.

Public finance has emerged as a leader in government-backed OSS, thanks largely to the move toward open banking. Regulations like Europe’s Second Payment Services Directive (PSD2) require banks, either directly or indirectly, to adopt open-source APIs for consumer products. As these projects mature and grow, their benefits and risks become increasingly clear.

Most traditional fraud detection systems are slow, inaccurate, and outdated. Machine learning models can react swiftly and adapt constantly, making them the ideal alternative. Developers who properly train and implement these algorithms can prevent fraudulent activity at a scale never before seen. 

Why Use Machine Learning Models for Fraud Detection?

Fraud is growing more severe every year, causing increasingly significant damage. Online payment fraud caused $41 billion in e-commerce losses in 2022 alone. Conventional detection systems are no longer effective.

Artificial intelligence-driven test automation is the future of software quality assurance (QA) because it has proven far more efficient, accurate, and effective than other methods. Although widespread acceptance is only beginning, adoption rates will likely soon increase.

Have Developers Embraced AI in Software QA?

Most professionals can attest to the impact AI has had recently. As of 2023, 40% of companies plan to increase their spending on it. That said, software QA hasn’t yet embraced its potential.

Can businesses apply the DevOps approach to Salesforce app development? DevOps techniques can work great with the Salesforce suite if you know where to start. Salesforce even has some features that can enhance DevOps strategies. How does DevOps apply to Salesforce, and how can businesses combine the two? 

How DevOps Applies to Salesforce Apps

DevOps, or Development and Operations, is one of today’s leading innovative software and service development approaches. Many businesses strive to apply it to apps and software platforms they’re already using or looking to adopt, including the popular CRM suite Salesforce. 

The gig economy represents a modern work environment where short-term, flexible jobs are prevalent and companies hire independent contractors and freelancers instead of full-time employees. This model has significantly impacted the developer community. They now have the freedom to choose diverse and dynamic projects, often working remotely.

This shift is changing how developers work and manage their careers, learn new skills, and balance work and personal life. Its growth opens new opportunities and challenges, reshaping the very landscape of software development.

5G is revolutionizing digital twin technology, enabling faster data transfers, real-time monitoring, seamless collaboration, and advanced security. These features are advancing the capabilities of digital twins and the value organizations can gain from them. What are the benefits of building a digital twin simulation on a 5G network? 

Capability for Large-Scale Real-Time Monitoring

Low latency and high bandwidth are the top benefits of 5G. One exciting application for these features is real-time monitoring at scale. 5G can handle expansive IoT networks. It can transmit large quantities of real-time data and continue to do so efficiently as an organization scales up its IoT infrastructure. 

The continuous integration/continuous delivery (CI/CD) pipeline encompasses the internal processes and tools that accelerate software development and allow developers to release new features. However, many parts of the CI/CD pipeline are automated. That’s a good thing because it accelerates workflows and reduces development or testing time. However, it also exposes the pipeline to cyberattacks because the automation does not require continuous monitoring. 

Here are some things to do to keep the software supply chain secure by protecting the CI/CD pipeline. 

Optimizing the software development cycle is becoming increasingly crucial as the world relies more on digital solutions. Rapidly digitizing industries like manufacturing need reliable, feature-rich, and secure platforms, but conventional dev practices can’t always meet these needs. DevSecOps could be the answer.

DevSecOps combines development, operations, and security workflows instead of having these teams work one after another in silos. Testing, collaboration, and security tweaks are constants throughout the process instead of the last steps. As a result, this workflow provides the agility and safety manufacturing operations need.

Since technology’s rapid advancement outpaces conventional professional growth, teams must seek additional learning opportunities to succeed. Lead software developers give their employees the tools to succeed when they invest in them.

Why Should You Promote Continuous Learning?

You should promote continuous learning opportunities because they are essential for your industry and professional advancement. Software development techniques and skills are in a state of constant growth, so your employees should be, too. Stagnating in a technical role ensures you will fall behind the rapid pace of technological advancements.

Manufacturing is in the middle of a technological revolution. The data-driven fourth industrial revolution, Industry 4.0, is in full effect, and as manufacturers embrace this change, their needs are shifting. The longer this trend continues, the more it becomes clear that the sector needs edge computing.

The manufacturing industry is already a leader in cloud adoption, but the cracks in the conventional cloud are starting to show. Production facilities need to take things further by embracing the edge, and there are five primary reasons why.

Whether you’re a developer actively looking for work or are just thinking about where your career might take you, keep a broad perspective on your options. You might initially think it makes the most sense to primarily work for companies that are seemingly most in need of your core skills — such as tech and app development businesses. However, in today’s highly digitalized and online-centered world, most companies eventually need software development expertise to achieve their goals. 

Hiring Growth Is Happening at Nontech Companies

Recent tech layoffs have made international news. It’s easy to begin believing things are getting tough for software developers if you only focus on that. However, a 2023 Bain & Co. report showed nontech companies hired more tech team members during the second and third quarters of 2022 than their technology business counterparts. 

Even something as abstract as software has real-world consequences. Data centers consume an estimated 1% of global energy and these power-hungry servers represent a mere fraction of IT’s total energy use. It’s time for IT developers to get serious about reducing their carbon footprint.

Going green can take many forms, including writing better code, making physical hardware changes, and changing workplace culture. IT professionals can use the following techniques to minimize environmental impact.

Most data scientists are well aware of the positive impacts their work can have on organizations. However, it can be challenging to convince people who don't work in data science of those benefits. 

Some of them resist doing things differently because they dislike changes. Others balk at the perceived high upfront costs of data analytics technology and the time required to develop a related strategy. Here are some useful things to mention when trying to sell these individuals on the need for data analytics.

The world runs on data. Data scientists organize and make sense of a barrage of information, synthesizing and translating it so people can understand it. They drive the innovation and decision-making process for many organizations. But the quality of the data they use can greatly influence the accuracy of their findings, which directly impacts business outcomes and operations. That’s why data scientists must follow strong quality assurance practices.

What Is Quality Assurance?

In data science, quality assurance ensures a product or service meets the required standards. It refers to verifying data is accurate, complete, and consistent. The data must be free of inconsistencies, errors, and duplicates, and the scientists must properly organize and document it well.

As more companies combine Internet of Things (IoT) devices and edge computing capabilities, people are becoming increasingly curious about how they could use artificial intelligence (AI) to optimize those applications. Here are some thought-provoking possibilities.

Improving IoT Sensor Inference Accuracy With Machine Learning

Technology researchers are still in the early stages of investigating how to improve the performance of edge-deployed IoT sensors with machine learning. Some early applications include using sensors for image-classification tasks or those involving natural language processing. However, one example shows how people are making progress.

The blockchain space has gained considerable momentum over the past few years. Cryptocurrency remains this technology's most widely recognized use case, but new applications and benefits emerge as it grows. For example, supply chain optimization is one less glamorous but highly impactful use case for blockchains.

Almost 90% of enterprise leaders are looking into blockchain technology in some capacity, and more than a third aim to use it in their supply chains. In addition, many organizations are eager to implement new technologies into these networks in light of the lingering supply chain disruptions from the COVID-19 pandemic. Blockchain developers that serve this need could make a substantial impact.

Data governance is one of the most important undertakings for businesses today. Regulations like the GDPR and CCPA require organizations to have thorough insight and control over their data, and the costs of poor-quality information keep climbing. An effective governance strategy addresses both, but creating and implementing such a program is often easier said than done.

More than 90% of enterprise leaders plan data initiatives for the coming year, yet more than half report struggling to pull any business value from their information. Many of these companies realize the importance of data governance in achieving their goals, but their strategies frequently fall short. For many organizations, that's because they rely too heavily on manual processes.