Application Performance Management Is Critical To Business Success

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We have entered the “age of the customer,” an era in which the best approach for a company to build a sustainable competitive advantage is to become customer-obsessed. The catalyst for success will be how businesses utilize increasingly innovative technology-based business services to engage external customers and enhance internal workforce productivity. All survey respondents clearly understood the definition of IT monitoring technologies and had partial or final decision making authority for these solutions. The study captured insight into the benefits and challenges with today’s IT monitoring solutions as well as the types of monitoring features that will help them meet application and performance and availability expectations. Results from the custom survey were supplemented with Forrester’s market data related to this topic.

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Brennan IT

For 20 years we’ve been committed to making our clients’ lives easier. It all started with a realisation that Australian businesses could be so much more — if only they were able to leave IT to the experts and focus on what they do best: their business. We do it by offering the mid-market exactly what they need: professionalism, know-how, simplicity and the same passion for their business as they have.

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Effects of Artificial Intelligence on Software Development

Article | August 23, 2021

What’s the core of those drone-supported Amazon deliveries, online food orders, the ability to watch your favorite shows on Netflix, and virtually augmented monitoring of your upcoming trip to Disneyland? Software! They constitute a significant part of almost every evolution we see around us. But how are the developers managing to yield so much from computer programming? How are they able to enrich so many lives through their creations all over the world? The answer is simple — Artificial intelligence (AI). Undoubtedly, AI is one of the leading technologies now, and it has the power to transform every bit of any business’ functionality. The software industry is not behind in making the most of AI and delivering intelligent and intelligent software. On the contrary, modern enterprises are convinced to adopt an entirely new software development paradigm to stand out from the competition. Traditionally, machine learning was predominant in the Software Development Lifecycle (SLDC). Even though it could encode numerous tasks in a computer program, it took relatively more time to be finalized. It required developers to put the exact requirements together first and hand them over to engineers. And then, engineers programmed the code accordingly. However, AI came with its advantages. As a result, it is reshaping the modern world of automated testing, Agile test software, and ultimately the entire software development. So if you see bots accompanying computer programs to make software development even easier, faster, and smarter in the future, it will be because of AI. So if you are already thinking of potential changes AI will bring to your software development process and how you can reap all the benefits of AI software development, stay tuned! Area of AI Software Development Artificial intelligence has a significant impact on various aspects of software development, for example, software testing, coding, designs, etc. Let’s now discuss what role AI will play in the current and future of software technologies by reshaping the major software development areas. Software Design Process will Improve Designing software is one of the most complicated and error-prone stages of software development. Therefore, specialized skills and the right experience are crucial for designing and planning software development projects to come up with an absolute solution. Moreover, the software designs are mostly subjected to dynamic changes as clients may suggest changes in different stages of software development. AI-powered systems such as AIDA (Artificial Intelligence Design Assistant) can eliminate such complexities in the design process. Time & Money Saving Software Testing Traditionally, software testing takes a lot of time, especially when there are changes in the source code. Plus, it's costly, too! But in the end, it’s one of the essential software development stages as it ensures product quality. Therefore, there’s no room for error. Thankfully, there’s AI and a variety of software testing tools. Testers can utilize them to develop test cases and carry out regression testing. This kind of automated testing is relatively faster, smarter, and astonishingly time and money-saving. On top of all, it's error-free! Easy Data Gathering and Analysis Data gathering and data analysis are the most fundamental stage of any software development lifecycle and need a significant amount of human intervention. The project team has to come up with all the information necessary for the software development, and clients' input can be dynamic. Automated data gathering through various AI tools such as Google ML Kit can be the best option to ease the process. It can take care of specific data-gathering processes without the need for significant human intervention. Say Bye to Manual Code Generation Generating huge codes requires a lot of labor, time, and money. Therefore, simplifying the code generation process is significant because code writing is crucial for any software development life cycle. While traditional code generation can fall short in identifying the target goals effectively, automated code generation can be a game-changer. This is because AI tools typically generate snippets of reusable codes and write code lines as instructed. As a result, they save a substantial amount of money, labor, and time. Benefits of Artificial Intelligence in Software Development Incorporating artificial intelligence in software development can do wonders. Considering the incredible impact of AI on software development and the possibility of incredible transformations in the future software technologies due to AI, here are some promising benefits of AI software development. Enhanced accuracy in estimates Conceptual decision making Error-free end product Easy bugs and error detection Improved data security Conclusion The software development landscape is rapidly changing, and AI has a lot to do with it. Being an enterprise, you need to understand the benefits of AI and how it is enriching human lives worldwide. It's hard to deny the tremendous pressure on the current software development industry from the demand for applications. However, it’s one of the fastest-growing industries, and AI can simplify it with secure, unique, and scalable solutions. Unquestionably, AI software development is the future, and adopting it is the best decision enterprises can make. Frequently Asked Questions What are the things to consider when adopting AI for software development? It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining What are the real-world examples of integrating AI into software development? Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C What are the top machine learning and AI tools software developers should consider? Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the things to consider when adopting AI for software development?", "acceptedAnswer": { "@type": "Answer", "text": "It would help if you consider the following factors to reach new heights with AI software development: Cloud is necessary for AI AI solutions are much more than implementing machine learning algorithm AI is near real-time or real-time Big data is required for AI Machine learning-powered AI solitons may need frequent retraining" } },{ "@type": "Question", "name": "What are the real-world examples of integrating AI into software development?", "acceptedAnswer": { "@type": "Answer", "text": "Here are some examples of AI tools that several organizations are using for efficient AI software development: Deep Code Stack Overflow AutoComplete Google Bugspot Tool w3C" } },{ "@type": "Question", "name": "What are the top machine learning and AI tools software developers should consider?", "acceptedAnswer": { "@type": "Answer", "text": "Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development: Google Cloud’s AutoML Engine Kite AIDA Testim.io IBM Watson Amazon Alexa Cortana TensorFlow Azure Machine Learning Studio" } }] }

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A Guide to Technical Debt Management

Article | August 23, 2021

In the ‘new normal’ world, digitalization is the core reason to drive businesses to success. As a result, these emerging technologies have given rise to technical debt. Of course, technical debt has always been a part of every IT and non-IT organization. But the evolution of the IT world has made companies invest more time to think about reducing tech debt. As the term consists of the word- debt, it gives a picture of being negative. Thus, technical debt is more or less like financial debt, but it also has good aspects. In this article, we will talk all about technical debt management and ways to deal with it. What Is Technical Debt? When a code design is not neat, has disjointed data structures, and is of poor quality, it gives rise to technical debt. If a code is designed in a hurry to meet strict deadlines, it may be written clumsily. For that particular period, the software may work perfectly well, but it starts bugging and stops working with time. And it becomes a difficult task to decode the coding and understand the problem as the code was not written neatly. The accumulation of work due to short-term decisions, unrealistic deadlines, shortage of resources, or just ‘lazy work’ leads to technical debt. Every organization has technical debt. Take note that this debt can never be zero. But the important part is to deal with technical debt management. It cannot be ‘paid off’ entirely but can be minimized and managed. Types of Technical Debt Various reasons lead to technical debt. Some are deliberate, and some are accidental. Whatever is the reason, if you notice any of the below actions implemented in your organization, consider it as a red flag. Then, identify the reasons for technical debt for precise technical debt management. Complex Architecture This is one primary reason that leads to technical debt. Of course, the coding should be precise and straightforward. But sometimes, due to limited time and resources, a complex architecture is created even for the simplest code. And if not mended with time, this leads to various problems along with technical debt. Thus complexity in products, processes, and applications should be simplified with time. Software Entropy This is also known as bit-rot. Software entropy occurs when developers write codes for the updates of the same software. As a result, the coding gains maximum complexity. And after a point, it gets unsolvable. Thus the software quality deteriorates, leading to errors and reduced usability. The solution to this is refactoring, which we will discuss later in the article. Accidental Tech Debt This type of tech debt occurs when there is sudden evolution in technology. For example, if the code was simple and precise but not meant to be adapted or updated with the changing scenario, accidental technical debt occurs. It leads to a wastage of time and resources in the future. Thus, this debt is resolved by dedicating time to update the code and system quickly. Ways to Handle Technical Debt Management When the engineering or developer’s team takes shortcuts to meet deadlines or finish the project early, it leads to more work in the long run. We understand that sometimes these demands are unavoidable, but the team needs to come back and resolve the issues to save the organization from technical debt. For starters, an organization must identify the causes of technical debt and deal with them accordingly. Here are some of the ways of technical debt management and how to manage them in the future. Identify the Source The best way to avoid and tackle technical debt is to identify the source. This can be done by having a conversation with the team as to why this debt occurred? For example, if the IT team was asked to design healthcare software within two months due to the project's urgency, the team will say that the reason for complex coding was the time constraints. As a result, you are identifying technical debt sources. They may have occurred due to time, resources, or system constraints. Once you identify the root cause, there is always a way to go back and solve the issue to avoid getting buried in technical debt. This is the first step towards technical debt management. Track Technical Debt You will be appalled to know that 7% of organizations track technical debt to minimize them. And they are a significant success. Tracking technical debt is as important as identifying it. When a team keeps track of technical debt, it is easy for them to forecast the time and resources required to pay it off. Digital Documentation This is building a roadmap while writing code. It makes sure that the entire team is on the same page while developing the software. The benefit of this documentation is that whenever there is an update or an error wherein the entire code requires to be reworked, it gets easier for any developer to work on it. This is a precise way to prevent the tech-debt burden. Maintain Agility & Testing This technique is paving the way for technical debt management. However, developing agile software and maintaining its agility through time are two different things. To reduce the technical debt, you need to maintain software agility by updating it on a timely basis. But be sure to make this update process clean and qualitative. Try not to skip testing of the software for whatsoever reasons. Testing of the software is essential to stop the build-up of technical debt. However, do not substitute manual testing for automated testing. It can prove to be a significant hurdle in the team’s agile process. Refactoring Dedicate time to understand and pay off technical debt. Even when you avoid errors and follow all the suitable approaches, there are chances of technical debt. Sometimes these may happen due to the client’s urgent demands or limited bandwidth to perform tasks. Whenever this happens, take out time and treat technical debt as a business problem and not a technical one. This is an intelligent way of handling technical debt management. Refactoring means going back to the developed codes in haste for many reasons and cleansing them. This does not affect the external working of the code but cleans the code and makes it future-ready. Furthermore, such timely cleaning of codes reduces technical debt significantly. How Is Technical Debt Good? Technical debt is not always bad. Sometimes the code building is done in haste and works precisely the way it should. The updating of codes is also seamless. At such times technical debt is regarded as a good thing. This usually happens in applications that have simple processes. Technical debt is also good in software that requires timely development and not bug fixes. The Conclusion Technical debt is required to stay ahead in the competition. But see to it that it is tackled and minimized in the best possible way. Procrastination is the enemy of success. And this is highly true in the case of technical debt. It will get more expensive the longer you hold it. So ensure that your team takes out regular time from their schedule for minimizing technical debt. Technical debt management will help your team to perform better, improve agility and deliver exceptional results. Frequently Asked Questions What drives tech debt? Tech debt occurs when the team cannot write a clean code due to deadlines or limited resources. The tech debt worsens if the team does not go back and dedicate time to clean the code. How do you handle tech debt? There are specific ways to handle technical debt. These include: Identifying the reasons that led to technical debt Integrate metrics in your strategy Digital documentation Refactoring Knowing that tech debt is not always bad What are the types of technical debt? There are majorly three main types of technical debt. Deliberate creation of complex software Software entropy Accidental tech debt { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What drives tech debt?", "acceptedAnswer": { "@type": "Answer", "text": "Tech debt occurs when the team cannot write a clean code due to deadlines or limited resources. The tech debt worsens if the team does not go back and dedicate time to clean the code." } },{ "@type": "Question", "name": "How do you handle tech debt?", "acceptedAnswer": { "@type": "Answer", "text": "There are specific ways to handle technical debt. These include: Identifying the reasons that led to technical debt Integrate metrics in your strategy Digital documentation Refactoring Knowing that tech debt is not always bad" } },{ "@type": "Question", "name": "What are the types of technical debt?", "acceptedAnswer": { "@type": "Answer", "text": "There are majorly three main types of technical debt. Deliberate creation of complex software Software entropy Accidental tech debt" } }] }

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Phishing Health Care: Prevent Criminals from Taking Advantage of Emergency Situations

Article | August 23, 2021

Phishing and email-borne malware has a disproportionately large impact on the healthcare industry. Among the 3,950 breaches examined in the 2020 Verizon Data Breach Investigations Report, 521 took place within health care organizations, making the industry the most frequently victimized among those included in the report. Financially-motivated criminal groups are particularly likely to target health care organizations, and human error often plays a role in their success.

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Create a Bulletproof IoT Device That Thrives in A Competitive Environment

Article | August 23, 2021

If you think the conventional way of designing and testing an Internet of Things (IoT) device is still relevant today, you might be wrong. Tens of billions of IoT devices surround us today. Billions more will connect to the internet in the next few years. On top of that, IoT deployment is diversifying from consumer-based to mission-critical applications in the areas of public safety, emergency response, industrial automation, autonomous vehicles, and healthcare IoT. While IoT devices offer great convenience, having large numbers of them in a small space increases complexity in device design, test, performance, and security.

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Spotlight

Brennan IT

For 20 years we’ve been committed to making our clients’ lives easier. It all started with a realisation that Australian businesses could be so much more — if only they were able to leave IT to the experts and focus on what they do best: their business. We do it by offering the mid-market exactly what they need: professionalism, know-how, simplicity and the same passion for their business as they have.

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