Software Development T-K Use Case
3 min read
This post is a part of the T-K Use Cases series, highlighting the means by which T-K can be used in various industry settings.
Key Challenges in Software Development
As a relatively young discipline, software development has remained adaptable to how it approaches workflow. Innovations driving a transition from waterfall to agile development have allowed the tech industry to manage the volatility in the field. Indeed, as post-pandemic society begins to take shape, tech companies that adjusted to unprecedented obstacles are likely to retain some new practices, such as remaining in a remote or hybrid work setting. This introduces additional complexity on top of the challenges that continue to plague the field. Here are the key challenges facing software development teams in 2021:
Slow development cycles: where are the bottlenecks in our process, and how is it impacting the software development life cycle (SDLC)? Even with agile practices, iterating through a cycle of development and performance evaluations can still feel slow and full of blocking items that hinder progress and force developers to context switch.
Repetitive work: where in this process are there opportunities to reduce unnecessary effort, and who is likely to benefit from it? Maintenance chores and project management tasks are common, but not always necessary to allow the team to function well. This can feel particularly repetitive and laborious when working remotely.
Resources that are unaligned with needs: how do we organize work in a way that best makes use of the full range of skills on our team? While T-Shaped developers are sought out as valuable resources, the diversity and breadth of knowledge available to a project makes it challenging for a team to identify skilled specialists, self-organize, and still allocate tasks optimally.
Low visibility into progress towards goals: how are these challenges evolving over time? A plethora of tools exists to help developers work, including issue-tracking software (Jira) and version control services (GitHub). However, few tools exist to conduct real-time aggregations that allow a team to stay abreast of inefficiencies that crop up. Instead, stakeholders are often caught reflecting on the success or failure of a project during static performance evaluations after the fact.
T-K Solution – an Intelligence Engine
T-K by CKM Analytix provides actionable insights towards increasing efficiency, accelerating development cycles, and fostering employee satisfaction on a real-time basis.
Figure 1. T-K’s Modules
By integrating with and ingesting from all relevant data sources, T-K is able to feed the AI models that have been fine-tuned in CKM modules over the years:
The Effort Model is a set of proprietary CKM algorithms to measure development activity on an individual basis. By regulating measures of human effort across different types of work, the model provides a comparable, quantifiable, and fair estimate of true effort. This allows project managers to make real-time decisions driven by their actual operations, rather than relying solely on the abstract story points that arise from planning poker.
The Process Mining module reveals bottlenecks in the development cycle. It provides users with the ability to conduct deep dives into interactions between different stages of development, sprints, the teams involved, and developers themselves. By bringing in the Effort Model, you can quantify the potential opportunities related to unproductive rework and process conformance.
Figure 2. The Process Mining Module
Visualizes stages of the software development cycle to identify bottlenecks and inefficiencies
The Repetitive Work module identifies patterns and types of unnecessary manual work that tends to scale with the product. These cases often represent opportunities for automation or outsourcing. A truly lean form of software development would draw attention to this during sprint retrospectives, and free up the team to pursue work that provides enduring value to the product.
The Category Metrics module analyzes the aptness of a developer’s skillset for different types of work assigned to them. By consolidating free-text fields such as descriptions, comments, or even commit messages, T-K can identify opportunities for the continued training of developers. It also empowers teams to better allocate work to skilled specialists during sprint planning.
Conclusion – Tracking Progress in T-K Real-Time
By linking the narratives surfaced by each of these modules, T-K allows users to diagnose and streamline the development pipeline dynamically. Indeed, it’s important to avoid interpreting insights as static snapshots in time and instead monitor the impact of adjustments as an ever-evolving trend. T-K is the real-time intelligence engine that uncovers operational opportunities and monitors your progress towards leveraging them to achieve your development goals.