How do you deal with making sure your use of new technology is correct and free from code-smells?

Responding to How do you deal with making sure your use of new technology is correct and free from code-smells, security issues, etc.?:
Issues can be dealt with in several ways.

Understanding what makes high-quality, maintainable code would be first, so knowledge of best practices regarding OOP, SOLID, design patterns, API design, etc. is important. Depending on what you mean by security, best practices in those regarding transfer protocols, coding styles, validation, storage, etc. are equally something one can learn.

Planning your work is useful, as a well thought out design is easier to implement, or at least will avoid future problems, than when you are just 'winging it'. Diagramming and project plans can be useful at this stage. Self-management is part of this, so using boards and epic/stories/tasks to track work is important, and there are free tools like Visual Studio Team Services (VSTS) or Trello to help.

Requirements gathering will matter so documentation and communication with users and/or clients will make a huge difference. Also, usability matters, so both understanding how to build code for others, whether it is a UI or an API will be important, to keep your clients happy and to avoid rework. With a UI, mockups can be useful, so using Balsamiq or Viso to put together the basics can be a starting point for discussing with users.

It would depend on your code stack. I work primarily in the Microsoft stack, so there are maintenance tools built into Visual Studio (VS) to check for code quality/maintainability and for code clones. Purchasing licenses for products like ReSharper can help. As part of the automated build process, VSTS has components for testing, code quality (Resharper), and build quality, executed on check-in

Independent of the stack, using TDD or unit tests are important, besides saving you time and effort. As an independent, it's tough to work in pairs, but code review can be useful, so enlisting someone to review your work can help.

Do Algorithms Make You a Better Developer?

Responding to a question on HashNode, Developers who practise algorithms are better at software development than people who just do development. Is it true?, I wrote the following:
My feeling is that algorithms help make one a better programmer, but that is likely true of many coding concepts. I did not have algorithms as an undergraduate, so my knowledge is acquired through reading and practice, but after reading and applying Algorithm's in a Nutshell, I felt the quality of my work improved. That said, my development work increased more after understanding Design Patterns, or after consuming books on database design. 
Since many types of knowledge improve developing and architecting abilities, one has to consider how it helps and to what degree. Algorithms are coding-in-the-small, often narrowly focused solutions, but which can have a great impact at scale. For many applications, a focus on algorithms would be overkill as data sets and requirements do not require it. In this context, any middling programmer can optimize a basic loop for performance. Proper database design, either relational or OLAP/OLTP, will make your applications better, from both maintenance and performance perspectives. Object-oriented programming makes some type of designs better, those that add objects, while learning correct functional programming helps in contexts where you are increasing functions on a limited number of objects. Learning enterprise architecture helps in the design of large scale operations. 
One could equally argue that learning and practicing self-management, communication skills, and code management all make for better programmers, and they do. Ultimately, learning makes one a better developer.

My Self-Development in 2017

My corporate annual review period recently passed, and I was reminded of all the skills developed and completed tasks over the past year, both in and out of work. Sincerely, remembering what I've done over the past year makes me feel good, and really reminds me of how much I enjoy learning.

Video Courses

Although largely focused on reading to learn, I do partake of various streaming video resources via Pluralsight. The courses I've completed this past year:

Multiple courses on management and leadership

Quantitative & AI-related courses, accompanied by work in R, Python, or VBA


Software Development

Work and Periphery


To avoid repeating myself, here are My Most Popular Posts of 2017.


Review: Complex Adaptive Systems: An Introduction to Computational Models of Social Life

Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity) Complex Adaptive Systems: An Introduction to Computational Models of Social Life by John H. Miller
My rating: 4 of 5 stars

A thought-provoking introductory exploration to modeling social systems, covering ideas for rule-based agents within a variety of rule-based systems, moving onto evolutionary-like automata and organization of agents to solve problems. Underlying some of the ideas, one could see references to deeper concepts, e.g., nonlinearity, attractors, emergence, and complexity, none of which was explained explicitly. At times, I did find the writing tedious, as some ideas were too obvious to spend time detailing, but overall, a well-written easy to digest text.

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My Most Popular Posts of 2017

Although I've made many posts on my Data Analytics Workouts site in the past year or two, some generated more interest than others - nothing here was virally popular - so I've written a post listing the most popular ones. Here is a link to the post.
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