AI Writing, CHATGPT, Google Gemini, Software Testing Templates

Google Gemini Prompts for Change Control Logs

Posted on by Alan Thompson
11
Jun

This is Part 8 of the Klariti Primer on using AI for Software Testing.

Welcome back to Klariti’s Primer on AI for Software Testing. In our previous article, we explored how AI tools such as Google Gemini, Claude, and CHATGPT can be used to enhance the management of Issue Logs.

Now, we shift our focus to another critical document in managing software project during testing: the Change Control Log. If you’re new to software testing, think of this log as the central nervous system for tracking all proposed and approved changes, detailing their impact on scope, cost, and timelines.

Learn more about the Change Control Log

Challenge: Maintaining a ‘Living’ Log of Change Impacts

Software projects are rarely static. As testing progresses, new requirements emerge, existing ones are clarified, or defects necessitate modifications. Each change, no matter how small it seems, has the potential to impact the project’s scope, the resources required (cost), and the delivery schedule (time).

The core challenge with the Change Control Log is not just recording that a change occurred, but diligently and accurately tracking its assessed and actual impacts.

Manually updating this log for every Change Request Form (CRF), ensuring consistent impact assessment, and keeping track of dynamic priorities (e.g., High, Medium, Low) and statuses (Approved, Evaluation, In Progress, Cancelled) can be a demanding and error-prone task. How do you ensure your Change Control Log is a reliable, up-to-date instrument for decision-making, rather than a static, historical record?

Scenario/Context: When the Log Fails to Reflect True Impact

Consider a project where several “minor” scope changes were approved via CRFs. Each was logged, but the “Impact on Schedule” and “Impact on Resources” sections in the Change Control Log were often filled with cursory notes like “minimal” or “to be absorbed.”

Individually, the impacts seemed manageable. However, the cumulative effect of these changes wasn’t clearly visible in the log. As the release deadline approached, the test team found themselves significantly behind schedule, struggling to cover the expanded scope. A review of the Change Control Log didn’t immediately flag the aggregated impact because the individual entries downplayed it.

This scenario underscores the need for a log that not only records changes but also facilitates a clear understanding of their individual and collective consequences on project constraints, enabling proactive adjustments rather than reactive firefighting.

The AI Solution: Enhancing Data Entry and Analysis for Your Change Control Log

AI, particularly LLMs, can be a powerful assistant in populating and analyzing the data within your Change Control Log, making it more dynamic and insightful. While AI won’t directly manipulate Excel’s dynamic filtering or conditional formatting for priority, it can significantly improve the quality and consistency of the data that drives these features.

Key Change Control Log Aspects AI Can Assist With:

  • Summarizing Change Request details for concise log entries.
  • Brainstorming potential impacts on scope, cost (resources), and time.
  • Suggesting initial priority based on CRF information.
  • Drafting status update narratives.
  • Analyzing relationships between multiple changes.

1. Simple Prompts (Quick Log Entry Assistance)

Use these for drafting concise summaries or suggesting initial classifications.

What they achieve: Quickly populate basic fields or get initial assessments.

Example Prompts:

"Given this Change Request Form summary: [Paste summary of CRF], draft a concise 'Change Description' (max 30 words) for the Change Control Log."

Rationale: Ensures brevity and clarity in the log.

"Based on the CRF stating 'Critical security vulnerability fix required within 48 hours', suggest an initial Priority (High, Medium, Low) for the Change Control Log entry."

Rationale: Helps in quickly flagging urgent items.

"The change involves adding a new reporting screen. List 3 potential areas of Scope Impact to consider for the Change Control Log."

Rationale: Quick brainstorming for scope considerations.

2. Advanced Prompts (Detailed Impact Assessment and Status Tracking)

These prompts ask AI to analyze CRFs more deeply or help articulate impacts and status.

What they achieve: Develop more comprehensive entries regarding impact and help in tracking progress.

Example Prompts:

"Analyze the attached Change Request Form [or paste key sections: Description, Justification, Proposed Solution] for 'CRF-123: Integrate new payment gateway'. Identify and list potential impacts on: a) Project Scope (e.g., new modules, changed interfaces), b) Testing Schedule (e.g., additional test design, execution, regression time), and c) Resource Requirements (e.g., need for specialized skills, additional tester days). This information is for populating the Change Control Log."

Rationale: Guides AI to think specifically about scope, time, and cost implications.

"Change 'CRF-089: Modify user authentication to support MFA' has moved from 'Approved' to 'In Progress'. The development team estimates 5 additional days of work. Draft a concise status update for the Change Control Log, including the estimated impact on the overall testing timeline."

Rationale: Helps articulate clear status updates and their implications.

"A Change Request proposes deferring non-critical defect fixes (Severity 3 & 4) from the current release to the next. For the Change Control Log, outline the potential positive impacts (e.g., on current release timeline) and potential negative impacts (e.g., on user perception, technical debt) of this scope change."

Rationale: Helps document a balanced view of a scope reduction change.

3. Complex Prompts (Strategic Analysis and Reporting from Log Data)

These prompts leverage AI to analyze multiple log entries or generate summaries for oversight.

What they achieve: Identify cumulative impacts, dependencies, or generate summaries for Change Control Board (CCB) meetings or stakeholder reports.

Example Prompts:

"Review the following 5 approved Change Control Log entries [Paste key details: Change ID, Brief Description, Estimated Time Impact]. Identify any potential cumulative impact on the project's overall release date. Are there any apparent dependencies between these changes that should be highlighted?"

Rationale: Uses AI to look for broader patterns and interdependencies across multiple changes.

"Generate a summary report from the Change Control Log for the upcoming CCB meeting. Focus on: 1) New changes submitted for 'Evaluation' this week. 2) 'Approved' changes still 'In Progress' that are impacting the critical path. 3) Any changes recently 'Cancelled' and the reason. [Provide relevant log excerpts or a structured list]."

Rationale: Assists in preparing concise, focused reports for decision-making bodies.

"The Change Control Log shows three high-priority changes (CRF-101, CRF-105, CRF-112) all impacting the 'User Account Management' module and collectively adding an estimated 15 days to the schedule. Draft a risk statement for the project manager regarding the concentration of high-impact changes in this single module and its potential threat to the Q3 release target."

Rationale: Helps articulate strategic risks based on aggregated log data.

Integrating AI into Your Change Control Log Workflow:

  • Augment, Don’t Abdicate: Use AI to draft impact assessments and summaries, but the final determination of scope, cost, and time impacts, especially quantitative estimates, requires human expertise and validation from project managers, development leads, and testers.
  • Feed it Good Data: The quality of AI’s analysis depends on the clarity and completeness of the Change Request Forms it’s processing.
  • Regular Review: Use AI-generated summaries to facilitate regular reviews of the Change Control Log, ensuring it accurately reflects the project’s evolving state.

Next Steps

Wew’re covered a lot of ground here. Let’s take stock of where we are. As mentioned, a well-maintained Change Control Log, actively tracking the impact of changes on scope, cost, and time, is indispensable for effective project governance during testing.

I’d recommend that you test the waters with AI to streamline the process of populating this log with detailed assessments and analyzing the cumulative effects of change, so you have a more informed decision-making and proactive risk management process.

I hope you found this helpful. Stay informed on best practices: For more insights and templates, ensure you’re subscribed to the Klariti Newsletter at https://klariti.com/newsletter/.

Next up: While the Change Control Log tracks the approval and impact of changes, the Change History Log provides a chronological record of all modifications made to specific documents or system components. We’ll explore how AI can assist in maintaining this detailed audit trail.

Templates (Free and Paid)

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Alan Thompson