Get more features delivered sustainably: as easy as connecting to Jira

Software developers often find new feature development to be the most enjoyable aspect of their work. It allows them to exercise their creativity, solve interesting problems, and deliver tangible results. The team’s product owner usually focuses on adding new features too. That, after all, is what customers want and what generates revenue. In this context, developers often prioritize feature development over bug fixing or code optimization, because "we are being paid for features, not for bugs."

Luckily there is now a solution that is as easy as connecting to Jira: Agile Analytics Sprint Insights.

However, this singular focus on new feature development can have detrimental effects in the long run. Software that is constantly adding new features without proper attention to quality and stability can become slow, unstable, and insecure. Neglecting bug fixing and letting technical debt accumulate leads to a deteriorating codebase and growing complexity. Over time, the software becomes hard to maintain and enhance. Moreover, security vulnerabilities may arise due to rushed development, lack of proper testing, or inadequate consideration of potential risks.

To address these issues, it is crucial to maintain a balance between feature development and non-feature work. Understanding the importance of allocating time and resources for bug fixing, code optimization, and security enhancements is essential for building a sustainable and robust software product.

Agile Analytics uses GPT and Large Language Models to tell feature work from non-feature work. It does so by analyzing the content and structure of work items or tickets. By processing the textual information within these items, the model can determine the category of work that best corresponds to each item.

The underlying language model, such as GPT, has been trained on a vast amount of text data and has learned to understand the context and meaning of words and phrases. When applied to Agile Analytics, the model can interpret the descriptions, titles, and other relevant information provided within the work items. In that text, the model finds the patterns, keywords and phrasing that mark a work item as feature development or as non-feature work. Bug fixing and code refactoring are typical examples of the latter.

Agile Analytics Sprint Insights further enhances this capability by allowing teams to train the model and fine-tune its performance.

The platform provides analytics for Sprint Review sessions or as a bi-weekly sprint report. With them, the team can assess the stability and sustainability of its software. Allow us to supercharge your software operation!