Software Engineering Insights

Drupal is amongst the most popular, open-source content management platforms. It has established itself in the market and has been growing since its launch. Community support for Drupal has made great strides and is always on track to stay up to date with the latest technology integrations. 

Technologies have become the primary vehicle of progress, which is true. However, there’s also the flip side of the coin: early subpar technological choices, if left unpruned, can result in technical debt. It may even lead to higher development costs, long time-to-market, and reduced productivity. With so much at stake, managing technical debt should be a priority both for CTOs and software developers.

Rolling out a new software system, infrastructure, or technology solution for an industry such as healthcare or personal finance has critical end-user outcomes. Technology Readiness Levels (TRL) refers to a robust scale often used by pioneering software organizations throughout their development efforts.

PyTorch and TensorFlow are two of the most widely relied-upon frameworks for Deep Learning (DL) research and industry implementation. While both these DL development frameworks haven't been around too long, they have made a significant mark in AI/ML research and innovation.  However, when it comes to which between PyTorch and TensorFlow is the better framework, there has not been a clear consensus.

The healthcare systems market has witnessed a large-scale shift towards more patient-centered care models in the recent past. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is one of the most reliable ways to measure patient satisfaction which can help digital patient-facing and patient-monitoring doctors' apps meet leading market standards.

Machine Learning (ML) is an application of Artificial Intelligence that has the maximum number of use cases, in almost every industry. Healthcare, automobile, marketing, finance, agriculture, retail- all of them are leveraging the power of ML to automate tasks and bring agility to operations. 

In an SDLC, there are several testing frameworks that help to assess if the application is progressing in the right direction. It could be in terms of usability, security, compatibility, performance, integrations, etc.

Artificial Intelligence, in practice, is complex. There are several technologies and training methodologies involved. There is a high risk involved in algorithm failure. With this,  businesses want nothing but the best of AI practitioners when it comes to executing the use of AI technology. That’s when the Artificial Intelligence (AI) Center of Excellence (CoE) comes into the picture. 

Organizations whose bottom line depends on quality software development need a robust methodology in place to assure bug-free product delivery for their clients. They must integrate a means to monitor the productivity and quality of software testing with a standard set of appropriate software testing metrics.

Most software systems of contemporary large-scale businesses function at full capacity as they have to deal with complex computations across distributed system architectures. In these cases, system failures have a high likelihood with the cause of failure remaining largely elusive. Chaos engineering is a field that is finding widespread application as a reliable means to be prepared for such failures in advance.