Generative AI in Software Engineering Needs A Thoughtful Evaluation Approach

CIOs are bringing AI into software engineering frameworks to drive efficiency, improve quality, and, ultimately, attain business objectives. Once they are past the following qualification criteria

? Use cases: Which problems are worth solving?

? ROI: Will the benefits outweigh the costs?

? Change Management: How disruptive is this?

? Security: Can we trust the system?

they need to establish a thoughtful evaluation approach to ensure success of the actual implementation.

Why Evaluation Matters

For a software engineering framework powered by Generative AI, evaluation is critical because:

1. AI-generated outputs must meet engineering quality standards. The output must first meet pre-defined acceptance criteria from an engineering standpoint.

2. The underlying models must be assessed and improved continuously. Outputs of the models need to be assessed and improved to avoid bias, ensure trustworthiness, and handle edge cases.

This means evaluation must happen at two levels: the output quality and the adequacy of the models themselves.

Level 1: Evaluating the quality of AI-generated output

Let us take as an example—a test scenario generator. Imagine an AI-powered tool that takes requirements and generates test scenarios. It is a value-adding concept, but it needs to produce test cases that are usable for automation. To evaluate its effectiveness, we can use:

? Coverage – Are all relevant test cases covered?

? Accuracy – Do the generated scenarios match business logic?

? Readability – Can all consumers (BAs, developers, quality engineers) easily understand them?

? Consistency – Would AI generate the same scenario across iterations for the same input?

Level 2: Evaluating the models themselves

Traditional NLP metrics like BLEU/ROUGE scores, Precision & Recall etc. will not be enough to assess the models if they have been built on Generative AI. We must consider metrics which are more attuned to LLMs. Since LLMs generate text dynamically, we can consider metrics like:

? Hallucination Rate: Is the model output completely unreal? E.g. test scenarios that are not realistic (for a requirement related to an auto insurance claim submitted online, one test scenario is generated as claim being automatically approved without claim validation)

? Diversity Score: Does the model generate varied (but valid) outputs? E.g. test data that does not cover all age-groups for premium modeling.

? Prompt Sensitivity: How much does the output change with minor tweaks to the prompts? E.g. if slightly rewording a user story causes the model to generate wildly different BDD scenarios, it is a sign that fine-tuning is needed (Prompt 1: “…when the claim is valid” and Prompt 2: “claim with no issues”, generating unrealistic scenario for Prompt 2)

Bias & Trustworthiness

Generative AI models do not just reflect biases, they amplify them. An evaluation framework therefore must:

? Check for bias: If the model is generating scenarios, is the output more biased towards certain edge cases over others?

? Ensure fairness: If the model is generating data, does the data show any lack of fairness in terms of race/gender/ethnicity?

? Implement human-in-the-loop validation. Have SMEs review the outputs that are specifically important for Business.

Designing for Self-Healing AI Using Prompt Engineering

While the metrics offer a way to measure and monitor, we must be proactive about ensuring the quality of the model output. Generative AI is probabilistic. It will have failure points. The question is, how can we help the model recover?

A self-healing AI system should be able to:

? Detect its own inconsistencies. Can it flag outputs that contradict previous responses?

? Refine outputs dynamically. If a test case lacks detail, can the model expand it based on historical patterns?

? Use feedback loops for continuous improvement.

Using a Second-Layer Prompt for Self-Validation

A second-layer prompt can be introduced to act as an automated quality control check before delivering the final model output.

Example: Self-Healing BDD Scenario Generation

Prompt 1 (Initial Scenario Generation)

“Generate a BDD scenario for a failed online transaction due to insufficient funds.”

Model Output (Initial Version)

Scenario: Failed Transaction Due to Insufficient Funds

Given a user initiates a payment ?

When the system checks the account balance ?

Then the transaction is declined ?

Prompt 2 (Self-Validation Check)

“Analyze the above BDD scenario. Are there missing steps, edge cases, or potential inconsistencies? If so, suggest improvements.”

AI Self-Check Response:

? Missing user notification step.

? No logging of the failed transaction.

? No alternative action (e.g., retry prompt).

Final Model Output (Self-Healed Version)

Scenario: Failed Transaction Due to Insufficient Funds ?

Given a user initiates a payment ?

When the system checks the account balance ?

Then the transaction is declined ?

And the user is notified of insufficient funds ?

And the system logs the failed attempt ?

The second prompt improves the accuracy of the model output by detecting gaps automatically and suggesting updates.

Best Practices for an Evaluation Framework for Software Engineering powered by Generative AI

A. Use a Two-Layer Evaluation Approach

? First layer → Validate output generated by the Generative AI model.

? Second layer → Validate the performance of the Generative AI model itself.

B. Introduce Self-Validation Prompts

? Use prompt engineering to review model outputs.

? Automate prompt refinement to catch missing details and refine final output.

C. Implement Human-in-the-Loop Review

? Have SMEs pay attention to high-risk validation points.

D. Ensure AI Transparency & Explainability

? Can engineers understand why AI suggested certain test cases or created particular test entities?

? Build traceability into model outputs.

Final Thoughts

Building a software engineering framework powered by Generative AI is exciting, but an AI model is only as good as the evaluation process behind it. Without structured validation, adoption will be incomplete. A thoughtful evaluation approach addressing engineering success criteria as well as model output accuracy is critical.