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Summary
This summary is machine-generated.

This study introduces a proactive learning algorithm for software requirements elicitation, reducing user queries and speeding up the process. The FAKT/Q algorithm uses both user input and domain knowledge to automate software specification.

Keywords:
Multiobjective optimizationcoevolutiongrammatical inference.multioracleproactive learningrequirements specificationsearch-based software engineering

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Requirements elicitation is a crucial, yet often manual, step in software engineering.
  • Automating requirements specification can empower users to define software systems independently.
  • Current active learning methods for this task require numerous user interactions.

Purpose of the Study:

  • To develop a partially automated approach for software requirements elicitation.
  • To reduce the number of user feedback queries in synthesizing requirements from examples.
  • To enhance active learning by incorporating external domain knowledge.

Main Methods:

  • Applied grammatical inference and active coevolutionary learning to synthesize requirements from user-provided behavioral descriptions.
  • Extended active learning to a multi-oracle setting, incorporating a "user oracle" and a "knowledge oracle".
  • Developed and evaluated the "first apply knowledge then query" (FAKT/Q) algorithm, comparing it against standard active learning.

Main Results:

  • The FAKT/Q algorithm significantly reduced the number of required user queries.
  • The proposed approach substantially sped up the requirements inference process.
  • Demonstrated the effectiveness of proactive learning in software requirements engineering.

Conclusions:

  • The FAKT/Q algorithm offers a more efficient and user-friendly method for software requirements elicitation.
  • Proactive learning, by leveraging domain knowledge, is a promising direction for automating complex software engineering tasks.
  • On-The-Fly Markets provide a practical application scenario for this multi-oracle learning approach.