Improving the Accuracy of Quality Assurance With Defect Tracking

Defect tracking and management is a crucial step in the software development lifecycle, which improves the probability of high-quality end products.

Quality assurance and software maintenance can be highly costly for the company; in many instances, companies are spending around 80% and even 90% (in some cases) of the software development budget on quality maintenance and keeping up with the standards. Defect tracking and management is a crucial step in the software development lifecycle(SDLC), which improves the probability of high-quality end products.

The later the defects are identified in the development stages, the higher the cost is likely to be to resolve them. In the traditional waterfall system, defect management was considered a small part of the development cycle and generally an afterthought. More often than not, defect tracking and management are presumed to be carried out in silos and are allocated only to the quality assurance department. The problem for the organization originates when defect tracking tools and strategies are not embedded in all stages of the development process and are conducted in isolated teams instead of a collaborated team effort.

The Challenges of Defect Management

Within the context of teams and people, isolating the defect tracking and management for the engineering and quality assurance teams creates a hindrance to the quality assurance process. The lack of a two-way feedback system regarding the issues and defects creates communication gaps. This also generates issues with regard to traceability and visibility of the issues, as the defects that are originating from the requirements or production phase require feedback from the relevant team members to resolve the issue.

However, if only one department, quality assurance, is responsible for addressing all issues, then the context and real cause might get lost in the translation. Different kinds of issues from various modules or components should be addressed through specific tools and strategies, instead of one standardized technique, which especially becomes an issue without the involvement of all teams.

In addition to that, the lack of an adequate defect tracking tool equates to serious pitfalls for the quality and timely delivery of the project as defects may not be categorized and sectioned properly. It is important that all defects and issues can be traced through the software application history, and relevant facts and history associated with the said issue can be accessed at any time.

This is not only helpful in case of a similar issue in a new project, but the line of connection of any defect to the right component or unit allows the team to gauge the most problematic or defect-prone areas. Through this, trends and patterns about the particular defects can be identified and noted in order to reduce testing efforts and generating a predictive model to avoid similar issues in the future.

Contribution of Defect Tracking and Management

The role of defect tracking in the quality assurance and software development cycle is substantial considering that it comprises important historical information and facts about the defects. Through the previous information and data available about common issues and defects, defect tracking systems and models can be produced, and with the use of sophisticated techniques like machine learning, they can be enhanced to predict errors even before they happen.

A hybrid of techniques and methodologies from artificial intelligence and machine learning can be used to train data sets and target defect-prone areas of the application specifically. The capabilities of the defect tracking models can invariably be enhanced for specific tracking and better visualization.

The defect tracking system also creates a platform for common knowledge and shared language which makes communication and collaboration between different users like developers and testers easier. The two-way communication channel which is established across the organization ensures that all users and teams have the same perception and knowledge about the prevailing issues in the system. Advanced filters and classification of data in the defect tracking system create a proper structure for all the information and history that is stored in the system.

There are usually two kinds of metrics that can be utilized to measure and determine the progress, quality, and, productivity of the system and which can be embedded in the defect tracking system as well.

Result metrics: These are the metrics that measure the process and activities which have been completed.

Predictive metrics: These kinds of metrics are derivatives that measure and identify the warning signs which can predict unfavorable results early.

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