CW Developer Network


June 17, 2019  8:06 AM

What is software feature management?

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

We need to look at software feature management and define the term.

But first, some context is needed to understand what kind of companies are operating in this space.

CloudBees describes itself as an enterprise DevOps leader — but is there a home user hobbyist and amateur version of DevOps hiding somewhere?

Perhaps CloudBees should stick to calling itself a Continuous Integration (CI) & Continuous Delivery (CD) company.

There’s no ambiguity there now, is there?

Regardless of corporate spin and messaging then, what’s happening in the Continuous Software Delivery Management System (CSSDM – not a real term, but let’s use it anyway) right now?

Straight outta the CloudBees new hive this month is the firm’s acquisition of Rollout, a secure ‘feature management’ company focused on developers.

So what is the feature management function and how does it fit into developers’ lives?

What is software feature management?

To define the practice itself, we can say that feature management a code release methodology designed to facilitate experimentation and trial Proof of Concept (PoC) augmentations and enhancements to existing(and in some cases new)  software application code bases. Used extensively by DevOps teams, software architects and programmer/developers of all sub-disciplines, software feature management makes use of so-called ‘flags’, ‘switches’ and ‘toggles’ which serve to decouple new features from the main code base release — specific test users are targetted to trial new features using a system of targetting rules, so that only those users will be able to see use that new feature. Flags alert users that a new feature has been added, switches and toggles (sometimes also called flippers, or conditional features) turn that feature on or off depending on its success and adoption.

Software feature management is clearly a key advantage for Continuous Integration (CI) & Continuous Delivery (CD) – that is, when software is moving fast, it allows us to separate code deployments from feature releases.

In theory, at least, this allows customer [user] relationships to enhanced by safely testing features in production environments, gathering valuable feedback and releasing new and improved features continuously.

Big-bang-or-bust

Back to CloudBees, the company argues that an increased number of incremental feature releases enables fast and early learning. This helps speed up feature adoption and ultimately helps organisations to avoid the ‘big-bang-or-bust’ infrequent feature release syndrome.

According to Sacha Labourey, CEO and co-founder of CloudBees,The acquisition of Rollout gives CloudBees customers the flexibility to decouple features from software versions. Using Rollout on their trusted enterprise platform allows developers to test and merge changes with more confidence than ever before.”

According to Forrester, experimentation platforms let teams test out minimum viable products (MVPs) in real-world scenarios with precise control.

“Feature flags let teams develop code as they normally would, allowing them to version control, test and deploy the same as always. However, feature flags give those teams complete control of when to expose a particular feature to users, allowing administrators to turn it on and off at the flip of a switch,” noted the analyst house.

CloudBees says that continuous release feature drip is the hallmark of successful continuous software delivery. Who knew anything drippy could be so cool?

Image: CloudBees Rollout

June 14, 2019  11:06 AM

The death of the data model?

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

Agile developers tend to view data modeling as a bottleneck preventing them from delivering value.

This is the view held by Pascal Desmarets, founder and CEO of Hackolade, a Belgium-based startup that focuses on the importance of proper schema design in microservices architectures.

As we know from TechTarget definitions, data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data needs to flow — the diagram can be used to ensure efficient use of data, as a blueprint for the construction of new software or for re-engineering a legacy application.

Desmarets insists that enterprise data models, though a worthy ambition, take too long to develop and are difficult to keep up-to-date.  

Model reinvention

He says that his firm proposes a new methodology to reinvent data modeling and make it relevant again in a developer-driven environment.

“Developers are correct in arguing that data modeling is not an end in itself. But a data model representing an abstraction of the requirements for information systems, becomes very useful if it leads to [a] good schema design. The schema acts as a contract between applications and becomes the authoritative source of the context structure and meaning, leading to higher data quality and better coherence throughout. This is critical to making sense of the huge quantity of data being accumulated and exchanged to feed Machine Learning, AI and BI,” said Desmarets

He reminds us that some have advocated a ‘code-first’ approach and embraced ‘schema-less’ databases.  

But, says Desmarets, the typical lifespan of applications is much shorter than the lifespan for data — and storing unstructured data is not an end in itself either.  

It appears that many companies realise that data quality and data consistency are higher with a ‘design-first’ approach, as long as the process remains Agile while using the dynamic schema nature of NoSQL databases.

“For data modeling to be Agile, a number of traditional techniques need to evolve. In particular, conceptual modeling should be replaced by Domain-Driven Design, a software development approach created by Eric Evans. DDD enables teams to focus on what is core to the success of the business, while tackling complexity in software design with a collection of patterns, principles and practices,” said Desmarets.

He thinks that while conceptual and logical modeling were well adapted to waterfall development and the rules of normalisation used in relational databases, Domain-Driven Design is better suited to aggregates and denormalisation used in APIs and NoSQL databases.  

Bypass logical modeling

It is also, argues Desmarets, an opportunity to bypass the logical modeling step, which is no longer needed since DDD concepts map directly to NoSQL data structures.

“By combining Domain-Driven Design with pragmatic process flowcharting, plus wireframing of application screens and reports, analysts and designers can easily apply  a query-driven approach to application-specific dynamic schema design,” said Desmarets.

So it appears, if what Desmarets is suggesting holds water, that the nature of data modeling is changing in some places.

The lifecycle used to be heavily front-loaded in a serial process. Today, data modeling takes place before every Agile sprint… and throughout the lifetime of an application. That’s this year’s model for data modeling.

Image: Hackolade


June 14, 2019  8:39 AM

Achieving balance in Machine Learning development

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

This is a guest post for the Computer Weekly Developer Network written by Ian Greenhalgh in his role as UK & US managing partner at Delaware.

Spelled in lower case, delaware is a software systems consulting organisation with specialist skills in SAP S/4HANA and Microsoft technologies — the company works across multiple industry verticals including automotive, chemicals, food, retail and the wider professional services sectors.

Greenhalgh contends that employees today in modern forward-thinking organisations are increasingly frustrated that the ‘intelligence’ available to them in their personal life isn’t available to them in the workplace

But, he says, his company is seeing technology being applied in a number of ways to help counter this shortcoming.

Greenhalgh writes as follows

Intelligence is happening. Robotic Process Automation (RPA) technologies and software-based bot’s are being implemented to automate mundane repetitive tasks, many of which were traditionally performed by service centres.

RPA is also being used to automate the handling of process exceptions, the reason being that it’s often cheaper to automate an unwanted process variant than to try to eliminate it.

Another burgeoning use case is the identification of emerging issues within large operational datasets, Machine Learning (ML) is triggering alerts to managers highlighting potential problems within a production process or a supply chain — issues that the user might not have spotted unless they looked for them specifically.  

This requires AI specialists to work with client process experts to identify a set of problems and then create the necessary training, development and test datasets which can be used to train and prove the ML algorithm. The challenge is to identify concrete data sets that allow the algorithm to recognise the exception from the general process noise. In cases where relatively large numbers of problems exist, producing distinct and differentiable datasets can be difficult.

Steady, but solid (learning)

The approach is very much exception by exception, business issue by business issue, but the steady result is that the algorithms monitor increasingly more process exceptions.

Evidently, early problem identification is a huge opportunity, but a balance needs to be struck. If the machine hasn’t been trained to deal with certain problem types or patterns, an overly reliant user might not be alerted to the problem until it is too late.

The answer to whether machines could ever completely replace humans is, unsurprisingly, sometimes.

But, we must stay vigilant of the two biggest issues; getting humans to trust the results provided by the machine and managing (and understanding) the consequences of failure – although 80% of Netflix content is selected by algorithmic recommendations, the consequence of failure is very limited, whereas if an algorithm fails within an airport scanner or a CAT scanner, the consequences can be fatal.

Let’s think about the real world application of ML and how it will affect us and keep automating.

Image Source: delaware

 

 

 


June 12, 2019  10:38 AM

Cisco’s DevNet Walk-Run-Fly developer play

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

Cisco is not a cables, hubs, switches and routers (did someone say token ring adapter?) focused networking company, Cisco is a software company focused on developers – right?

Well, such is the trend and Cisco has been using its events to showcase its DevNet developer programme (it’s developers Dev + Net network goodness, get it?) and explain its component parts… many of which are focused on (wait for it, here comes another trend that every vendor worth its salt is following) automation advantages.

The company thinks that the role of the networking professional has changed as it moves away from manual, time-intensive tasks and into the world where IT, DevOps and application and cloud developers work together.

SVP/CTO and founder of Cisco DevNet is Susie Wee is enthused about the prospect of bringing DevOps practices to networks. She claims that her firm is bringing software skills to the networking industry with new Cisco DevNet certifications.

Cisco’s Intent-Based Networking Systems (IBNS) portfolio enables secure network automation at scale with a new community-based developer center called Cisco DevNet Automation Exchange.

But what is intent-based networking? You may well ask.

Fundamentally, intent-based networking embodies the concept of the network administrator (and/or sysadmin) being able to state, describe and define the preferred optimised state of the network’s operational status for any given period of time (or defined data workload execution) with sufficient network orchestration policies, platforms and tools in place in order to be able to maintain that status.

So then, back to Cisco… the DevNet Automation Exchange features sharedcode (yep, they used one word) repositories of use cases for network automation. Cisco’s community of developers, networking professionals, partners and customers can actively contribute software to create this repository for network automation code.

According to Cisco’s Wee, “Another thing that our networkers told us is DevNet has been great in helping us with learning how to code and learning to use Cisco APIs, but we’re ready to take it further – we need help solving the use cases we face every day. We are addressing this is by developing software that solves use cases for network automation on top of Cisco products and APIs. Rather than write all the code ourselves, we are bringing the software practice of shared code repositories to network automation.

The company explains that DevNet Automation Exchange guides teams through their journey to implement network automation and intent-based networking with a Walk-Run-Fly methodology”

  • Walk: Get visibility and insights in their networks. This enables use cases such as gathering telemetry and insights from running networks, and performing auditing to ensure ongoing security of the network.
  • Run: Activate policy and intent across different network domains. This enables use cases such as providing self-service network operations that comply with security policies and operational guidelines.
  • Fly: Proactively manage applications, users, and devices with DevOps workflows. This enables use cases such as deploying applications in continuous integration and continuous delivery (CI/CD) pipelines while configuring the network in accordance with new application policies.  

In related news, VP of Cisco’s training and certification Mike Adams detailed the company’s new certification and training.

Adams notes that the firm’s training programme is now evolving with a focus on educating network engineers and now software developers to optimise new networks.

Learning materials applicable to the new DevNet certifications are available on the DevNet developer site.

Cisco’s Wee: Go big on network automation.

 


June 12, 2019  7:40 AM

WP Engine DevKit: promises a straightforward ‘slick sandbox’

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

WordPress platform company WP Engine wants to be known as the WordPress Digital Experience Platform (DXP) company.

Is that experiences for WordPress end users, customers, partners or plain old developers?

Well, WP Engine would no doubt say ‘all of the above’, but in this instance, we’re looking at the needs of software application development professionals.

The organisation’s newly announced WP Engine DevKit combines a local development environment, SSH Gateway access, deployment help and other WordPress developer tools for building, debugging and deploying.

The WP Engine DevKit, currently available as an open beta, is free to download and works with any WordPress environment but is optimised for WP Engine.

It is currently available for Mac & Linux in a command line interface (CLI) with a graphic user interface (GUI) version available soon.

WordPress is of course the most popular content management system (CMS) in the world, making up over 34 percent of all websites, but WordPress development can be tough, in some cases.

Straightforward ‘slick sandbox’

Too much time can be wasted doing manual tasks debugging and figuring out how to make disparate tools, libraries and packages work together. Once all the tools are synced, workflow must be established to keep projects on track. These workflows, processes and tools can get in the way of actual development, bogging developers down and adding frustration to what should be a straightforward, productive process.

Founder and Chief Technology Officer at WP Engine Jason Cohen says that DevKit integrates a number of best-in-class developer tools to help solve these common issues encountered by WordPress developers.

“I’m delighted by how well integrated the WP Engine DevKit CLI is with WP Engine’s environment,” said Jon Brown, CEO at 9seeds. “It makes common steps easy, like being able to pull the entire site and database down to local in a single CLI command, while providing super powerful developer tools like a built-in man-in-the-middle proxy to inspect all outgoing requests in detail.”

The benefit of being able to run a WordPress development environment locally gives developers a quicker way to iterate because everything is self-contained on the computer, eliminating the need to wait for uploads, downloads, etc.

This ‘sandbox’ is supposed to give developers a safer way to experiment with their sites, allowing them to experiment but not affect the site or your production environment in any way. It also offers offline functionality so developers can work anywhere, even where WiFi may be spotty.

Prospective customers will be able to download it via the DevKit landing page and learn more about the WP Engine DevKit here.


June 10, 2019  10:03 AM

Women in code series: Denise Gosnell, DataStax

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

The Computer Weekly Developer Network and Open Source Insider team want to talk code and coding. But more than that, we want to talk coding across the diversity spectrum… so let’s get the tough part out of the way and talk about the problem. 

If all were fair and good in the world, it wouldn’t be an issue of needing to promote the interests of women who code — instead, it should and would be a question of promoting the interests of people who code, some of whom are women.

However, as we stand two decades after the millennium, there is still a gender imbalance in terms of people already working as software engineers and in terms of those going into the profession. So then, we’re going to talk about it and interview a selection of women who are driving forward in the industry.

Denise Gosnell, DataStax

Dr. Denise Gosnell is head of the Global Graph Practice at DataStax. As an Interdisciplinary graduate education research traineeship (IGERT) NSF Fellow, Dr. Gosnell earned her Ph.D. in Computer Science from the University of Tennessee and a Masters of Science, Mathematics: Graph Theory at East Tennessee State University. Her passion centres on examining, applying and evangelising the applications of graph data and complex graph problems. 

CW: How well do you think graph is understood in the software developer space?

Gosnell: In how best to use your data as a graph, I think the biggest issue is not necessarily a misconception, but a gap. When I say that, I specifically see a massive gap in skills between understanding data like a graph and then using data like a graph… and this happens all the time.

We work with customers around the world, who want to explore graph technology generally. You will have your engineering team and some architects at a whiteboard and they will discuss their data problems and drawing out that some data is in a siloed system that links to data in another siloed system. They then quickly, as a team, realise that they might have a graph that is within their data within their enterprise.

Usually, you will segment a team that’s going to explore using graph technology to solve that problem.  But, that team quickly discovers there’s a massive and steep learning curve around implementing that graph database or graph structure. That steep learning curve comes from the ease at which they understood it at the whiteboard to actually using it in practice. To me, that’s the biggest misunderstanding that we have right now in the graph industry – it’s just navigating that big gap and having more tools to navigate the steep learning curve from idea to production application.

CW: So there’s a skills shortage essentially?

Gosnell: To me, it’s not exactly just a set of skills. There’s a mindset shift that we need to educate people about and, yes, that’s a skill, but it is also a new way of thinking.

Specifically, this is a mental shift from thinking about the entities or objects in your data to prioritising the relationships across your data. We call this evolving from entity-first design — to relationship-first design.

With more advanced and mature technologies, sometimes skills can be developed by taking a few courses and becoming certified in a certain technology. We’re not exactly there yet in the graph world. We’re not at a point where if you need to become good at a certain API there’s exactly a course for it. More work needs to take place across the industry to develop understanding around graph.

CW: Do you think diversity is an issue in software and around graph specifically?

Gosnell: First off, I haven’t seen any data. Whenever I talk about diversity, I like to take a data-driven approach and see what types of surveys have been out there and what the respondents are saying. The 2019 Stack Overflow survey came out recently and all of the data uniformly across that survey is still indicating that we have a massive gap in diversity across tech.

Within the graph industry specifically, I have found personally – and this is more anecdotal and not data-driven – there are more women involved. I don’t have data-driven statistics today, but it would be pretty interesting to specifically attempt to have a survey to try and address that.

One thing that I can speak to is the data I am privy to see in enrollment trends in one department at one university. In 2012, I was the founding president of a group called Systers that had the mission to recruit, retain and mentor women in Computer Science at the University of Tennessee, where I am now an Industrial Advisory Board Member. What is interesting to note is that while the department has seen 3x growth in enrollment, the percentage of female students has not seen the same growth.

CW: In your day-to-day, do you work with a lot of women or have women colleagues who focus on graph in the enterprise software space? Plus, has that evolved in the last few years?

Gosnell: In the technical field here at DataStax, there are quite a few women in leadership positions and we do a lot of coordination on projects across the company. There are many women that I have the true honour to work with every day to help solve different problems for large companies around the world, where some of their teams are led by women who are solving graph problems within the very largest enterprises and organisations.

However, without that real data-driven survey, in my opinion it’s hard to really put a metric on the ground. From my purview we’re doing a decent job, but you can always do better.

CW: Did you have a female mentor yourself or someone who helped you navigate being a woman in enterprise software?

Gosnell:  It hasn’t necessarily been just one person – I’ve had a lot of mentors, both male and female – but the person who opened up my eyes to the world of graph theory was Dr. Teresa Haynes who was my Master’s advisor. She is extremely well renowned in the academic graph theory space. I started my work in graph theory with Teresa Haynes as one of my direct advisors in addition to Dr. Debra Knisley. So, at the core of my foundation when I got started, there were two very prominent female researchers who really helped highlight and pave the way for me.

CW: Is there one piece of advice you could give to the upcoming generation of aspiring women leaders in technology?

Gosnell: Out of context this could always get a little hairy, but in all honesty, one thing that I have always done is to ask for forgiveness, not permission. When I see opportunity and when I see a project that’s going to bring value to my company or to whom I’m working with, I go about getting as far down the process as I can to essentially create a proof of defensibility of its usefulness so that you can get that done.

I often spend time with those in my network talking about the differences in how men and women approach challenges all the time. We commonly see, in both professional and athletic environments, that there is a stark difference in how men and women approach problems and learning. Anecdotally, we find that men are willing to try anything regardless of how they appear during the process. On the other hand, we find that women want to understand the correct result before stepping up to the plate.

From my experience, the focus on the correct destination instead of the lessons learned along the journey inhibits the learning process and professional development. The focus on destination perfectionism, regardless of gender, is holding individuals back from learning, making progress and gaining experience. Though not exactly 50/50, I see this falling on a gender divide and it is detrimental to an individual’s progress.

CW: What will success look like for you in the future? What are you most excited about for the next year?

Gosnell: In the next nine months or so, I can’t wait to get my book finished and out the door. Dr. Matthias Broecheler my co-author and I are collaborating on an O’Reilly text to explain the mindset shift and implementation details of graph technology that we talked about earlier. Completing this book together is going to be a huge achievement that I started driving last year.

Longer-term, I think that graph data and graph technology has the real potential to be the next wave of innovation that’s going to transform the tech industry and there’s a lot of work between now and graph technology really solving those problems that we know that they can solve.

I’m really looking forward to graph technology solidifying and finding its place as a tool that’s mature. That’s a much bigger objective that I would see coming into play three to five years from now.


June 5, 2019  7:20 AM

Talend weeds out new highs for AI vision systems

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

Talend senior product manager David Talaga spoke to the Computer Weekly Developer Network this month to talk about data, programming, computer vision systems, advances in AI and garden weeds.

Using the now well-seasoned example of how computer vision systems learn, Talaga reminded us that a human infant might typically only need to see four or five dogs to be able to recognise a dog in the future.

But, as we know, training a computer to recognise a dog in an image – and eradicate false positives (computers can mistake a dog for a fox, or a coyote, a wolf, a dingo or a jackal) – is likely to require large data sets of hundreds of thousands of images.

He thinks that no matter how much technology improves, we’re unlikely to ever be able to train a computer vision system in the same way as a human baby because of the unacceptable margin for error which would be present.

Data integrity

So what’s the secret to feeding computer vision systems properly? Talaga says that when training machine systems, data integrity is key.

“Especially when data sets are often pulled from the Internet, appropriate data governance processes must be put in place to ensure the integrity of data used for learning and limit access to the data to those who need it. Because they are so mission critical and can be time-intensive to assemble, huge data sets used for training machines can also be very valuable – meaning that data security must also be a key consideration, particularly when companies and nation states are engaged in an AI arms race and looking to gain any possible advantage,” said Talend’s Talaga.

Talend cites a working example where AI has had an advantage over humans.

Farmers have a constant battle to recognise and treat weeds which harm their crops and reduce yields. Weeds can be very similar in appearance, so much so that humans can struggle to distinguish different varieties and therefore identify the most effective and least disruptive treatment.

Weedscout

Bayer Digital Farming has been working with Talend to build a computer vision system called Weedscout which enables farmers to send in photos of weeds and receive an immediate answer on weed type.

“Talend processes and combines farmers’ photos, information from farmers about the weeds and their crops along with geolocation information from the mobile device, plus other bundled XMP metadata… and stores this data in the photo database. Talend also sends this data to an image-recognition module, which uses neural networks trained by a database of weed photos. When a farmer sends in a photo, the app identifies it and sends the result back to the farmer’s mobile device, usually in less than a minute. The app can recognise nearly 70 different varieties of weeds by matching them with its database of more than 100,000 photos,” said Talaga.

The technology used for weed-image recognition is based on self-learning algorithms.

To help ensure the app’s answers are comprehensive and error-free, the image database must be fed with further weed images. To date, there are 100,000 photos uploaded in the database on a private AWS cloud, with 70 different varieties of weed identified to help farmers increase yields and profits through computer vision.

Wikipedia Commons

 


June 4, 2019  3:27 PM

The next tech skillset is ‘differently-abled neuro-diverse’

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

Software application developers are, generally (somewhat unfortunately) mostly white, male, pretty standard guys with good educations, a predilection for pizza and often somewhat questionable facial hair.

As we know, with all manner of women in technology, girls who code and female technology empowerment programmes now running across the planet, we are starting to change that imbalance.

So what comes next for diversity?

Once we embrace gender diversity (for all genders, including non-binary definitions) and of course make sure that we have accommodated for physical disabilities, we can start to look other areas such as differences in sexual orientation, cultural background or religious orientation and all other areas where inclusivity and belonging really matter.

After that (not to suggest that any one of the above should come before or after another necessarily), what comes next for diversity?

We can now look to also think about the abilities that ‘differently-abled neuro-diverse’ people can bring to the workplace and the world of software development and programming.

Differently-abled neuro-diverse

That’s not a term that comes up a lot and is perhaps something of a new notion, so here’s what we hope is an illustrative example.

SAP this week noted that HSBC is the 2019 recipient of the Klaus Tschira Human Resources Innovation Award for digitally transforming its Human Resources (HR) department, which looks after some 275,000 people around the globe.

Along with the award, SAP made a €10,000 donation to the National Autistic Society, the UK’s leading charity for autistic people and their families, on behalf of HSBC. This move comes in line with SAP setting itself a goal for employing some 650 people across the autistic spectrum globally by 2020 – a figure the company will no doubt revise over time as it goes forward.

“HSBC has made its employees the focus of its digital HR transformation,” said SAP SuccessFactors president Greg Tomb. “The inclusion of differently-abled, neuro-diverse people is a shared vision of both HSBC and SAP. Our own Autism at Work programme builds a culture of inclusion by recognizing the unique skills and contributions of autistic colleagues, and we are honoured to make this donation to the National Autistic Society.”

HSBC has used SAP SuccessFactors to change the way it manages and delivers its employee experience, from compensation and career development to succession planning.

The Klaus Tschira HR Innovation Award commemorates SAP cofounder Klaus Tschira’s vision to help organisations unleash the full potential of their employees.

Autism suits programming

According to Otsimo, some of the strengths we might often be able to identify in autistic people are below (we have selected those points which may be best suited to programming/developer roles) – you can click the link in this para to see the corresponding challenges too:

  • Strong long-term memory skills
  • Mathematics, computer, musical, artistic skills
  • Thinking in a visual way
  • Detail oriented
  • Independent thinkers less concerned about what others think of them
  • Non-judgmental listening
  • Extensive knowledge resulting from deep study in favourite topics
  • Understanding rules and sequences
  • An intensive focus when working on a favorite activity

The next developer skillset initiative could (and should) feature more differently-abled neuro-diverse people.

National Autistic Society — httpswww.autism.org.uk


May 31, 2019  8:29 AM

MuleSoft takes the donkey work out of API lifecycle management

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

Application Programming Interfaces (APIs) have become one of the new darlings of the cloud computing-centric world of interconnected, interlaced, interlocked technologies that we use today.

As the ‘glue’ that defines the way in which different elements of different applications and data resources are capable of talking to each other, APIs play an essential role in modern programming.

In daily use, API connectivity can be used to ‘expose’ a firm’s business capabilities out to a broader ecosystem of developers, partners and employees.

One player in the API space is MuleSoft — the company is known for its application network brand known as [the] Anypoint Platform.

This month sees MuleSoft launch Anypoint API Community Manager, a technology that combines an API ‘portal’ i.e. a hosted space where developer teams can collaborate on API lifecycle management, personalisation, support case management and engagement analytics.

No more donkey work

So it’s a case of aiming to take the donkey work (sorry – no other headline really seemed appropriate) out of all those (above) elements of API lifecycle management while also providing a channel for programmers to chat and interact.

If you want to get fluffy, MuleSoft says it means organisations can now build what it calls “connected digital experiences for their API products” with API Community Manager.

“An API programme is a coordinated effort to manage the creation and usage of APIs in support of a broader digital business strategy. With this release, MuleSoft is the first to productise [a process for] how to build a successful API programme by extending MuleSoft Catalyst with Catalyst Mobilize, a packaged set of proven best practices and methodologies built on experience working with thousands of organisations across industries,” said the company, in a press statement.

MuleSoft chief product officer Mark Dao says that until now, companies have had to use a combination of technical content management solutions and generic social engagement tools to build developer portals and drive engagement.

Dao asserts that API Community Manager can fuel an API ecosystems and increase engagement – all in a single product without writing any code.

Image credit: Wikipedia


May 30, 2019  8:26 AM

Women in code series: Pooja Tyagi

Adrian Bridgwater Adrian Bridgwater Profile: Adrian Bridgwater

The Computer Weekly Developer Network and Open Source Insider team want to talk code and coding.

But more than that, we want to talk coding across the diversity spectrum… so let’s get the tough part out of the way and talk about the problem.

If all were fair and good in the world, it wouldn’t be an issue of needing to promote the interests of women who code — instead, it should be a question of promoting the interests of people who code, some of whom are women.

However, as we stand two decades after the millennium, there is still a gender imbalance in terms of people already working as software engineers and in terms of those going into the profession. So then, we’re going to talk about it and interview a selection of women who are driving forward in the industry.

Pooja Tyagi

Pooja Tyaji is a test management consultant at NTT Data UK, a company that specialises in systems integration, application development and AI-enriched IT services.

CW: What inspired you to get into software development in the first place?

Pooja Tyagi: It’s 13 years since I was recruited as a graduate by a top, well-known IT organisation. Being from an electronics background, I’ve always thought software is the future in every field. I genuinely believe that this will be the topmost area any firm should be looking to develop in order to compete in the market. This is the reason I chose to be part of the software industry and chose software testing as my career.

CW: When did you realise that this was going to be a full-blown career choice for you?

Pooja Tyagi: That’s always been an interesting question for me. I was trained on Java and databases during my initial career, so whenever it was time to quality check the code and verify the product outcome, I was the first one to enjoy and raise issues with potential bugs. That was the moment that I decided to take software testing and QA as my career path.

CW: What languages, platforms and tools have you gravitated towards and why?

Pooja Tyagi: Being into testing, I always gravitated towards tools like Selenium and Jmeter; test management tools such as ALM and TestLink; defect tools like JIRA, Bugzilla. There are also cross-browser testing tools, mobile testing tools and database testing tools that I greatly admire.

CW: How important do you think it is for us to have diversity (not just gender, but all forms) in software teams in terms of cultivating a collective mindset that is capable of solving diversified problems?

Pooja Tyagi: That’s another interesting and very debatable topic in our industry these days. Although I have always worked in a field with very few females I don’t feel that I have ever failed to showcase my capabilities and prove my achievements. However, there is no doubt that the industry needs requires a mixture of varied technical expertise, strong leaders, and encouraging diversity is central to bringing that fresh talent into the industry.

CW: What has been your greatest software application development challenge and how have you overcome it?

Pooja Tyagi: From last-minute deadlines to team changes, in the IT industry, we can often face challenges and tough times. In fact, during the SDLC lifecycle, this can be as frequent as every day.

One personal example was in 2010, when I was working as Test Lead for a Japanese client. As well as being new to the role, I had limited interaction with the Japanese team due to the language barrier. Communicating with the team was difficult and could often lead to misunderstandings. The Japanese also have different working style, being very accurate and quality-oriented. This means that any gap or missing details from our side could raise serious concerns.

I took this on this challenge by using Google translator to communicate with the team and test their products. I also encouraged my team to do the same, which lead to us building a strong relationship with the Japanese members of our team and a final product that was successfully delivered with no live defects.

CW: Are we on the road to a 50:50 gender balance in software engineering, or will there always be a mismatch?

Pooja Tyagi: A female software engineer that I know was recently looking to change roles and was subsequently invited for an interview. Instead of focusing on her achievements and qualifications during the questioning, she was instead asked about the length of her marriage and whether she was planning for a family.

Being asked embarrassing, personal questions or being rejected for a role due to your gender means that those who do make it through to interview stage are put off or don’t get a role they deserve. Either way, it means that, potentially, the best person for the job does not get the role. This is a lose-lose situation.

Ideally, the managers who require new team members and perform these interviews should be impartial when they see a female candidate. Managers and interviewers should be trained not to behave in this way and similarly, candidates need to be empowered to call out this behaviour.

Based on this, I don’t believe that the mindset is 100% changed in India, but I have definitely seen a rise in female employees in my industry in last 10 years. Globally, we have some good female leaders heading up the organisation and team, which is a remarkable progress, but still we haven’t come to a 50-50 point yet.

CW: What role can men take in terms of helping to promote women’s interests in the industry?

Pooja Tyagi: As well as transparency on both sides during the interview process, I believe training should be given around how to help women, particularly those who have re-joined the company after maternity leave.

If they believe their team is not a balanced representation, men should raise this point with the relevant person within HR or talent teams. By raising awareness of this fact and making hiring women ‘normal’, the skills that female employees can bring to companies will be highlighted. Similarly, men can join groups that focus on promoting, supporting and recruiting more females.

I think one of the main ways men can help this issue is by being a source of support, advice and motivation for their female colleagues, listening to their issues and promoting their successes.

CW: If men are from Mars and women are from Venus, then what languages or methodologies separate the two (basic) sexes?

Pooja Tyagi: I have read and enjoyed this book many times. I agree that men and women approach tasks in a different way. Interesting, in my experience men adopt a waterfall methodology to tasks, whereas women typically use a more ‘agile’ approach!

CW: If you could give your 21-year old self one piece of advice for success, what would it be?

Pooja Tyagi: “Success never comes in one go. You need to be able to face challenges again and again and plan for the long term – you can’t assume things will go exactly as you thought. If you can do this then you will be successful.”

Tyagi: We need transparency on both sides, clearly.


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