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Artificial intelligence offers real value, but recognizing its limitations is critical for actually capitalizing on that value.

Artificial intelligence, or AI, is one of the most intriguing topics in software development today. It is also one of the most widely misunderstood. For software developers and IT teams, AI offers an array of tantalizing possibilities for making applications faster, more scalable and more efficient. However, in many cases, the hype surrounding AI doesn’t line up with the reality of what is actually possible or practical. Toward that end, here’s a look at five common AI myths related to software development and deployment.  

1. Artificial inteligence is a new technology.

AI has gained major attention from the media in just the last few years. Likewise, most software products that pitch AI as a key feature (like AIOps-driven monitoring tools) are still very new. But, AI is not a new technology at all. The concept of machine learning and artificial intelligence in general stretches back many centuries (see, for example, the Brazen Head). And software applications have been using AI to do things like play checkers since the 1950s.

Artificial intelligence, or AI, is one of the most intriguing topics in software development today. It is also one of the most widely misunderstood. For software developers and IT teams, AI offers an array of tantalizing possibilities for making applications faster, more scalable and more efficient. However, in many cases, the hype surrounding AI doesn’t line up with the reality of what is actually possible or practical. Toward that end, here’s a look at five common AI myths related to software development and deployment.  

1. Artificial inteligence is a new technology.

AI has gained major attention from the media in just the last few years. Likewise, most software products that pitch AI as a key feature (like AIOps-driven monitoring tools) are still very new. But, AI is not a new technology at all. The concept of machine learning and artificial intelligence in general stretches back many centuries (see, for example, the Brazen Head). And software applications have been using AI to do things like play checkers since the 1950s.DO YOU WANT ACCESS TO2019 TECHNOLOGY SALARY SURVEY FINDINGS?YES, UNLOCK ACCESSNO, NOT RIGHT NOW

Thus, if AI seems like a relatively new technology in the software world, or one that has only become practically usable in the past few years, that is only because it took the media and marketers a long time to catch up. The reality is that AI has been an established field of computer science for more than half a century.

2. AI is smarter than humans.

Some AI advocates would have you believe that AI-powered applications are “smarter” than humans, in the sense that they can solve problems or develop ideas more creatively and effectively than human minds. But the reality is that AI-powered software applications don’t outthink humans. They simply think faster than humans.

And when it comes to use cases that require nuanced understanding of reasoning and human expression, AI fares particularly poorly, as IBM’s recent experiment with AI-powered debate software showed.

There is a chance that this could change in the future. Someday, AI might become so sophisticated that AI-driven applications are genuinely smarter than humans. But that day remains beyond the horizon.

3. AI will lead to smaller IT teams.

Many marketers of AI-powered software tools sell their products as a way for companies to reduce the size (and, by extension, cost) of their IT teams. By using AI to automate IT decision-making and operations, they say, companies can do more with fewer staff members.

Some observers go so far as to claim that AI, combined with other innovations, is edging us closer to a world of “NoOps,” wherein IT operations teams are fully replaced by software.

It’s certainly true that AI can help to increase automation and reduce the manual effort required to perform certain tasks. However, the idea that AI will remove the need for human engineers entirely is fantastical. Someone still has to set up and manage the AI-powered tools that do the operations work.

Plus, there is an argument to be made that AI is not making IT operations simpler; it is merely helping IT Ops teams keep up with the ever-increasing complexity of new software and infrastructure. Deploying and managing containers and microservices requires much more work than dealing with virtual machines or bare-metal servers. In this sense, AI is simply helping IT teams to maintain the status quo; it is not empowering them to gain new ground.

4. AI software is “set and forget.”

On its face, AI tools can seem like a type of “set it and forget it” wonder. If data-powered algorithms and machine learning allow AI tools to make all the decisions they need, then humans don’t have to do any work beyond the initial set up and data training, right?

Well, no. There are lots of reasons why even the best-designed AI tools need to be managed actively and continuously. They need to be constantly retrained with up-to-date data in order to make accurate decisions about ever-changing conditions. The quality of the data that they rely on must be carefully managed to ensure that it delivers the level of accuracy and clarity that the tools require. Humans may need to help provide ethical guidancefor AI algorithms.

5. AI will destroy the world.

The four AI myths that I have discussed above involve hype or an excess of confidence in the abilities of AI and machine learning. Now, I’d like to approach things from the opposite perspective by pointing out that AI is not at all a bad or useless technology.

Sure, AI has many shortcomings, and AI tools in many cases are not likely to live up fully to the promises behind them. But that doesn’t mean that AI is the bane of our existence, or that software teams should not use it at all.

This is important to note because the conversation surrounding AI has so far tended to be bipolar in nature. On one side are technologists and futurists promising us that AI will lead us into utopia. On the other are fierce AI critics worried about an AI-driven dystopia marked by all manner of dehumanizing, unethical automations.

Neither of these views represents reality. AI will not fully replace humans, but it will make their jobs easier. AI won’t completely remove the need to perform manual tasks, but it will reduce it. AI won’t prove smarter than human beings, but it can provide insights that help them make smarter decisions.

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As technology becomes a more prominent and instrumental element of our daily lives, so does the underpinning software and platforms powering these systems.

Just as a computer needs an operating system to function to its full potential, IoT and network devices need firmware and all electronics need some form of software.

The growth of technology has introduced a concurrent increase in demand for better and more efficient software. This side-by-side growth has also pushed new professional opportunities, as more companies and operations look to bolster their software development teams.

But that doesn’t mean the market isn’t competitive  quite the contrary. Anyone who wishes to remain competitive in today’s landscape must keep their fingers on the pulse. New and innovative trends play a role in software development. Understanding where the market is going and what professionals can be doing to advance in the field is not just beneficial  it’s essential.

Here’s a look at some of the more influential trends making an impact in the software development field today.


The rise of robotics and advanced automation solutions is no secret. So many are discussing the idea that they might lose work to these new technologies and systems, which goes to show just how prevalent they are in today’s landscape. Experts project the industrial robotics market alone to grow by 175% over the next decade.

An often overlooked element of this whole situation, however, is that the software and control interfaces used to manage these technologies don’t yet match the demand. The lack of infrastructure becomes even more glaring concerning remote computing.

Think of it like this. Every robot or hardware system needs underpinning software to power its operation. But they also require nuanced controls, which property or facility managers will use to keep the equipment running optimally or making adjustments. That might be a local system, powered via on-site computers and servers, or it might be off-site systems — reliant on cloud computing and remote technologies. Then there’s “smart” or app-based controls accessible via mobile. Those kinds of control interfaces need someone to build them as well.

Any way you cut it, the rise of these technologies puts increased demand on software development circles. And that’s before even considering maintenance and continual improvement requirements. All these software solutions will need support long into the future through bug fixes, security updates, and general improvements.


Virtual reality (VR) and augmented reality (AR) technologies are becoming more popular as their capabilities increase. The worldwide AR and VR markets will likely grow over seven times their current size between 2018 and 2022.

These solutions offer many applications, particularly when it comes to hands-on or virtual training, instructional tasks or even research and development. Workers can don a headset to immerse themselves in an entirely virtual experience using VR tech. On the other side of the coin, they can bring digital information and content into the real world using AR tech.

For example, imagine a plumber or electrical technician being able to see all the wiring and pipelines hidden behind solid surfaces by wearing a pair of AR-enabled goggles. But as with all forms of modern technology, developers must create the software powering these devices and experiences.


McKinsey predicts the IoT market will be worth a whopping $581 billion for information and communications technology-based spending by 2020, at a compound annual growth rate between 7 and 15% during that same period.

The Internet of Things — or Industrial Internet of Things — is more of a network of similar devices, all designed to collect and transmit varying streams of information. They also present new, more efficient opportunities, like the ability to control related devices from afar or enable more informed automation processes.

Software developers and engineers will have to create all digital facets of these technologies, from the firmware used to power the tech to the interfaces and software used to control them.

Of course, when you’re talking about more vulnerable and internet-ready devices, the overall requirements are much more complex, too. Information security and high levels of privacy are necessary when it comes to the transmission and collection of all data. It’s not safe to exchange highly sensitive information openly, where competing operations or unscrupulous parties could harvest it.

Software developers will not only be responsible for creating these solutions but managing all aspects of them as well, including continued security and efficiency. Some longstanding professionals may even move into managerial or advisement positions using their accrued experience, talents and knowledge to further corporate interests. This shift is particularly notable for information security, as high-profile hacks and data breaches are incredibly common these days.


Coding and language requirements continue to lessen as time goes on, with the industry pushing towards more streamlined opportunities. In a survey involving 3,300 IT professionals, 41% saidtheir organization is already using a low-code platform.

For those unfamiliar, low-code development involves the use of drag-and-drop style interfaces, mitigating the need to understand programming and coding.

That doesn’t necessarily mean there’s a decrease in demand for professionals with programming backgrounds, especially since developers still need to engineer low-code environments in the first place. But it does help to show where the industry is heading in the next few years. More and more development operations will focus on using such platforms, so gaining familiarity with them is desirable.

It also shows software developers will need to interact more and more with non-coding types or inexperienced developers, which requires a certain finesse.


While the trends discussed here can and will have a significant impact on the future of the industry, they are not the only changes happening.

Additional trends include the rise of microservices and perpetual offerings, the spread of conversational UI and increased emphasis on AI and automation, as well as more capable remote and cloud computing technologies. Any of these trends could replace the others in priority and importance. It’s difficult to say with any degree of certainty what trends will rise to the surface.

One thing is always certain, no matter what trend or pattern is prevalent: The software development industry is continually evolving, and that’s never going to fade away.

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South Korean technology behemoth Samsung announced the release of its blockchain and decentralized application (DApp) Software Development Kit (SDK) in a recent post on its website.

Per the announcement, the Samsung Blockchain SDK allows for account management and backup, payment and digital signature facilitation, Samsung Keystore and other cold wallet support. The page dedicated to the SDK also explains that it is a superset of all SDKs, including the Samsung Blockchain Keystore SDK.

As Cointelegraph reported in February, the new Samsung smartphone, the Galaxy S10, includes storage for private cryptocurrency keys. In May, rumors started circulating that the tech giant would also be rolling out blockchain-enabled features to its budget smartphone models.

More recently, in June, Samsung’s IT subsidiary announced that it is launching three new products aimed at addressing clients worries about blockchain. The products aim to make integrating blockchain with other platforms easier for entities that are attempting to adopt the technology. 

The president and CEO of Samsung SDS has also revealed that the firm is including blockchain as one of the key technologies for its “Digital Transformation Network” in May.

Also in May, Samsung competitor and consumer electronics giant High Tech Computer (HTC) announced the Exodus 1S smartphone with Bitcoin (BTC) full node capability, and rolled out in-wallet cryptocurrency trading for users of its Exodus 1 smartphone.

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credits : Forbes

There’s no doubt that it’s a new era for digital marketing. The irruption of new technologies in the form of automation, artificial intelligence (AI)-powered tools and big data analytics has changed the marketing landscape.

In fact, I can’t recall a more favorable moment for marketing in my 10-plus years managing my software-focused IT company. With the aid of software development and new technological advancements, companies of all sizes can redefine their marketing strategies and boost their marketing team’s performance. However, doing so requires a shift in the understanding of what digital marketing is and can be, as well as a careful approach to incorporating new technologies.

Understanding Modern Marketing Trends

Knowing what modern marketing trends can bring to the table will help you determine whether they’ll work for your company. I think all of these will somewhat impact your brand in the near future, so it’s good to learn about them now to see what you can expect.

• Artificial intelligence (AI): AI is all over the place now and for a reason. With its aid, you can analyze consumer behavior and identify patterns through the use of data coming from social media and your website.

Working with AI software also allows you and your marketing team to make more informed decisions when it comes to defining the best channels for your communications, your sales outreach and the influence of your digital advertising.

A good example of AI supporting marketing happened when Samsung launched its S8 model. The South Korean company used social listening tools to monitor the reactions to the device by scanning words, hashtags and phrases of interest. The AI then explored the sentiment surrounding them and pulled actionable insights.

• Automation: Marketing automation is something you’ve surely heard about. It refers to software that helps you prioritize and do your marketing-related tasks more efficiently, saving time.

Automation can make a difference when it comes to communicating with your customers. Delivering predefined actions such as emails and offers based on specific behaviors can enhance your relationship with clients.

• Big data analytics: Modern marketing software allows you to delve into the data you gather through all channels. You can gain insight into your customers’ thoughts and behavior. It also provides a centralized platform where all of your data is collected, aggregated and stored for quick access for team members. That way, you can adjust your overall strategy to offer a more personalized experience.

Efficient software can monitor key performance indicators (such as unique visitors, leads, generation costs and return on investment (ROI)) and identify patterns and potential growth opportunities. Take H&M as an example. The fashion retailer uses data insights to improve its supply chain, while detecting trends that impact its inventory and defining prices. It also uses big data to tailor the merchandise for its stores.

• Personalization: Another big player in today’s marketing world, personalization through custom software lets you use all your available data to address each customer in a more tailored way.

You’ve surely had an experience with personalized marketing by now: special offers based on your purchase history, deals tied to your specific location and recommendations in accordance with your preferences. The possibilities for delivering personalized actions are endless.

Making The Shift To Marketing Based On Software

Simply being informed about the trends that are available isn’t enough, though. It is important to decide which ones will work for your particular needs. Not just that — you also have to manage your expectations around these trends and consider your own timing to ultimately decide if you are ready to embrace them.

• Understand your overall strategy and goals: One of the things I think every marketing team should do once in a while is to review its overall strategy and objectives to check if there’s a need to adjust them. For instance, you might feel tempted to automate marketing tasks, from email marketing to lead generation. However, this can possibly lead you to actions that won’t fit with your target audience.

• Think about how marketing software impacts your audience:Though your goals might be to get more leads, make more sales or improve internal tasks, you have to consider how marketing software can impact your customers. There might be a gap between what the software can give you and what your clients want.

• Be certain about what these trends can offer: Time and time again, I’ve spoken with people who were disappointed with one of these new trends that felt like the next big thing. Believing that automating marketing, gathering large datasets or personalizing your experience will boost your sales or increase your brand awareness just because you’re using them is plain wrong. Implementing these technologies into your marketing requires hard work, analysis and continuing adjustments for them to deliver on their promises.

• Analyze where you are standing right now: Another thing many advocates of these trends try to instill in everyone who’s listening is a sense of urgency. “You need to embrace these tech solutions for your marketing right now!” they cry, and many believe them — even if they aren’t ready for the switch. Don’t get caught up in a false sense of urgency.

Some Final Words

Using automated tools, AI-powered solutions, big data analytics and a personalized approach provide marketing teams with a variety of benefits. More efficient time-management, easier data control and access, deep insights available for decision-making and more autonomy for the team are just some of them.

However, all of these benefits are promises that are only fulfilled for companies that implement those tools with careful attention at the right time. Understanding what they can give you is only half of the journey — you need to devise how these trends will fit in your overall marketing strategy and how they will impact the way you do things.

Don’t rush into this new age of digital marketing simply because you don’t want to be “left behind.” It takes a lot of hard work and effort to truly understand the new landscape and how can you fit in it.

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Software developers have a lot to contend with when it comes to keeping their skill levels current. New features are constantly introduced to integrated development platforms, and programming languages evolve frequently, as do development methodologies, like Waterfall. On top of these moving parts, developers have to deliver projects on time and in budget, follow security coding best practices and remain compliant with industry regulations.

Many developers have adopted DevOps, the principle of integrating development and IT operations under a single automated umbrella, which streamlines frequent feature releases and increases application stabilityHowever, it can be difficult for security and compliance monitoring tools to keep up with this pace of change, as they weren’t built to test code at the speed DevOps requires. Security is largely regarded as the main obstacle to rapid application development, and as a result, application security in DevOps has suffered.

In the long term, applications with security built in from the beginning are far more likely to resist attack and avoid potentially devastating interruptions to daily operations. This is why the DevSecOps application security movement is so important — and why developers need to understand its relevance to their work and how it can improve their output.

Ryan O’Leary, former chief security research officer at WhiteHat Security said: “Our average customer takes 174 days to fix a vulnerability found when using dynamic analysis in production. However, our customers that have implemented DevSecOps do it in just 92 days.” Likewise, he added: “If we look at vulnerabilities found in development using static analysis, an average company takes 113 days, while the DevSecOps companies take just 51 days.”

The concept of shifting security left assumes everyone is responsible for security, in contrast to incident response, where the security team is called in at the end. Adding more automation from the start reduces the chance of misadministration and mistakes. Automated security functions, such as identity and access management, firewalling and vulnerability scanning, can be enabled throughout the DevOps lifecycle.

Security and risk management leaders need to adhere to the collaborative nature of DevOps to create a seamless and transparent development process. To do this, developers need to ensure security is included in all decisions and lifecycle processes.

This is very much a two-way street. Software developers need to fully understand current and proposed security processes and lifecycles. For example, where are the shortcomings in regard to adding security in?

Likewise, consider what is already in place to ensure application security in DevOps, as well as tools and skills that will need to be used and learned. For example, do developers have tools to check for vulnerabilities during the local build process, or do they do this in the continuous integration/continuous delivery process? Also, do company development processes mandate a security element check the code is secure at each build process?

Most organizations have clear governance of risk, and derived security policies are a product of that. Newer ways of thinking, such as making a transition to DevSecOps, require implementing processes to bake those security policies into DevOps processes. In order to produce positive results when baking application security in DevOps, organizations should combine development, security and operations teams, shorten feedback loops, reduce incidents, and define and emphasize shared security responsibilities.

The time it takes to fix a vulnerability is a good measure of whether your DevSecOps application security program is effective enough. This time frame reflects security team agility, as well as how they handle and prioritize issues that come up.

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The Indian Ministry of Electronics and Information Technology had earlier revealed India’s first home-made processor called as Shakti. The chipset has been in the works for since 2016 and now Indian Institute of Technology Madras has released an SDK (software development kit) for the processor.

IIT Madras’ RISE group has been responsible for the development Shakti and they have said that they plan on releasing six classes of the processor in the market. These six stages of the processor include E-Class, C-Class, I-Class, M-Class, S-Class, and H-Class. They can be used in a wide variety of devices  IoT, robotic platforms, motor controls and more.

The C-class of processors is a 2-bit 5 stage in-order microcontroller-class of processors and has a clock speed of 0.2-1 GHz clock speeds. The I-class is a 64-bit processor with multi-thread support and clock speeds ranging from 1.5 to 2.5 GHz. M-class processor can support up to 8 cores and has the same clock speed.

S-class variant of Shakti are aimed at server-type workloads and it happens to be an enhanced version of the I-class processor with the same multi-thread support.  The H class processor is for the high-performance computing and analytics workloads. Apart from that, RISE is working on T-class and F-class processors as well.

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The software as a service model has been widely embraced as digital transformation becomes the norm. But with it comes the risk of network outages. IDC has estimated that for the Fortune 1000, the average total cost of unplanned application downtime per year can range from $1.25 to $2.25 billion. This risk arises primarily from the rapid iteration of the DevOps methodology and the subsequent testing shortfalls.

To protect against certain errors and bugs in software, a new and streamlined approach to software testing is in order.

The DevOps/downtime connection

Development and testing cycles are much different than they used to be due to adoption of DevOps methodology. To remain competitive, software developers must continually release new application features. They’re sometimes pushing out code updates as fast as they are writing them. This is a significant change from how software and dev teams traditionally operated. It used to be that teams could test for months, but these sped-up development cycles require testing in days or even hours. This shortened timeframe means that bugs and problems are sometimes pushed through without the testing required, potentially leading to network downtime.

Adding to these challenges, a variety of third-party components must be maintained in a way that balances two opposing forces: changes to a software component may introduce unexplained changes in the behavior of a network service, but failing to update components regularly can expose the software to flaws that could impact security or availability.

Testing shortcomings

It’s pricy to deal with rollbacks and downtime caused by bugs. It typically costs four to five times as much to fix a software bug after release as it does to fix it during the design process. The average cost of network downtime is around $5,600 per minute, according to Gartner analysts.

Financial losses are a problem, but there’s more to be lost here. There’s also the loss of productivity that occurs when your employees are unable to do their work because of an outage. There are the recovery costs of determining what caused the outage and then fixing it. And on top of all of that, there’s also the risk of brand damage wreaked by angry customers who expect your service to be up and working for them at all times. And why shouldn’t they be angry? You promised them a certain level of service, and this downtime has broken their trust.

And there’s another wrinkle. Software bugs cause issues when they are released, but they can also lead to security issues further down the road. These flaws can be exploited later, particularly if they weren’t detected early on. The massive Equifax breach, in which the credentials of more than 140 million Americans were compromised,  and the Heartbleed bugare just two examples. In the case of the Heartbleed bug, a vulnerability in the OpenSSL library caused significant potential for exploitation by bad actors.

Developers make changes to the code that trigger a pipeline of automated tests in this environment of continuous integration and delivery. The code then gets approved and pushed into production. A staged rollout begins, which allows new changes to be pushed out quickly. But this also relies heavily on the automated test infrastructure.

This is hazardous, since automated tests are looking for specific issues, but they can’t know everything that could possibly go wrong. So then, things go wrong in production. The recent Microsoft Azure outage and Cloudflare’s Cloudbleed vulnerability are examples of how this process can go astray and lead to availability and security consequences.

A new way to test

A solution to the shortcomings of current testing methods would find potential bugs and security concerns prior to release, with speed and precision and without the need to roll back or stage. By simultaneously running live user traffic against the current software version and the proposed upgrade, users would see only the results generated by the current production software unaffected by any flaws in the proposed upgrade. Meanwhile, administrators would be able to see how the old and new configurations respond to actual usage.

This would allow teams to keep costs down, while also ensuring both quality and security, and the ability to meet delivery deadlines – which ultimately helps boost return-on-investment. For the development community, building and migrating application stacks to container and virtual environments would become more transparent during development and more secure and available in production when testing and phasing in new software.

Working with production traffic to test software updates lets teams verify upgrades and patches in a real-world scenario. They are able to quickly report on differences in software versions, including content, metadata and application behavior and performance. It becomes possible to investigate and debug issues faster using packet capture and logging. Upgrades of commercial software are easier because risk is reduced.

Toward quality releases

Application downtime is expensive, and it’s all the more painful when it’s discovered that the source is an unforeseen bug or security vulnerability. Testing software updates in production overcomes this issue by finding issues as versions are compared side by side. This method will save development teams time, headaches and rework while enabling the release of a quality product.

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Sourced, or source{d}, as the company styles its name, provides developers and IT departments with deeper analytics into their software development life cycle. It analyzes codebases, offers data about which APIs are being used and provides general information about developer productivity and other metrics. Today, Sourced is officially launching its Enterprise Edition, which gives IT departments and executives a number of advanced tools for managing their software portfolios and the processes they use to create them.

“Sourced enables large engineering organizations to better monitor, measure and manage their IT initiatives by providing a platform that empowers IT leaders with actionable data,” said the company’s CEO Eiso Kant. “The release of Sourced Enterprise is a major milestone towards proper engineering observability of the entire software development life cycle in enterprises.”

Because it’s one of the hallmarks of every good enterprise tools, it’s no surprise that Sourced Enterprise also offers features like role-based access control and other security features, as well as dedicated support and SLAs. IT departments also can run the service on-premise, or use it as a SaaS product.

The company also tells me that the enterprise version can handle larger codebases so that even complex queries over a large data set only takes a few seconds (or minutes if it’s a really large codebase). To create these complex queries, the enterprise edition includes a number of add-ons to allow users to create these advanced queries. “These are available upon request and tailored to help enterprises overcome specific challenges that often rely on machine learning capabilities, such as identity matching or code duplication analysis,” the company says.

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Developers in enterprise environments — and at commercial software companies, for that matter — have learned that to deliver features swiftly, it’s much more expedient not to reinvent the wheel with certain chunks of code. And so they increasingly build their software by mixing and matching open source software components within their code base to minimize their development time to coding the components that truly add value and differentiation to their applications.

This reliance on open source components greatly speeds up innovation but often comes at a high price: Many of these components available for download contain dangerous vulnerabilities. Some companies are better than others in establishing policies about how and when developers can use them, as well as at actively managing the components to track for flaws. The latest research shows that those that do it well can minimize the risks introduced by these components into their software while maximizing the gains.

“For organizations who tame their software supply chains through better supplier choices, component selection, and use of automation, the rewards are impressive,” says Wayne Jackson, CEO of Sonatype, which last week released its “2019 State of the Software Supply Chain Report.” This study, along with two others released in the past two months, paint a good picture of open source component risks and how organizations are mitigating them.

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In-person interviews can be an intimidating experience, but are necessary for the hiring process. One of the biggest mistakes a candidate can make is coming into an interview unprepared. Candidates should do their research on the organization and put themselves in the best position to land the job. 

However, this task is often easier said than done, especially for roles that are more technically-based like software engineers. These professionals may find they are able to show off their technical skills, but not necessarily their soft skills during the interview process. 

Soft skills are becoming more important to hiring managers and staff at tech companies, according to a recent Udemy report. Not only should candidates assess their own soft skills, but they should also consider the personality and culture of the company they are interviewing with, said Rishon Blumberg, founder of 10x Ascend. 

Keeping the focus on software engineers, “[they] should take stock of what are the most important elements for them in a new job,” said Blumberg. “When you have a clear understanding of what you value most (culture, mission, work flexibility, compensation, equity, etc) a natural roadmap will start to unfold as you meet with different companies.”

Preparing for the interview questions can be difficult, but candidates often stand out—or redeem themselves—based on the questions they ask. The awkward “do you have any questions for us?” at the end of the interview is a great opportunity for candidates to rise to the top. 

“Coming in with prepared questions will lessen your stress when you get to the end of the interview and ensure that you leave the interviewer with a positive impression of your interest in the company and role,” wrote Brian Wong, Pathrise software engineering advisor, in his post on Pathrise Resources. “Questions that show your willingness to learn, drive to work hard, and excitement about the company and mission are all really helpful for the interviewers to gauge your fit as part of the culture.”

Here are some of the best questions software engineers can ask during an interview: 

1. How would you describe your organization’s company culture?

“You’ll spend approximately 40% of your life at work, so working in an environment with a culture that matches your own beliefs and desired lifestyle goals is vitally important,” said Blumberg. Candidates need to make sure they’ll enjoy working with the people they are around. 

2. What is your onboarding process like for developers, and how quickly will I be contributing to the code base? 

Software engineers should inquire about the onboarding process and the time it would take to really start the job, said Paul Wallenberg, senior unit manager of technology services at LaSalle Network. This question shows purpose and drive in the candidate. 

3. What are your current challenges on the team? 

Candidates should ask about the challenges current team members face, said Wong. Through this question, the candidate can learn how the organization handles hardships and conflict. 

4. What kind of projects will I be given?

“Many job seekers in the tech sector are mission driven and looking to work on challenging projects,” said Blumberg. “They want to solve unique problems and create products that will help humanity in some way. If this is important to you, ask about what types of things you’ll be building and working on. Will these projects be challenging enough to hold your interest for the duration of your tenure with the company?”

5. What type of ongoing educational opportunities does your organization support?

Software engineer candidates should ask the hiring manager if the company offers education courses, or support for education courses, noted Blumberg. This shows the candidate’s willingness to learn, and is also valuable for candidates who are interested in continuing school. 

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