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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2017

Month Degree Awarded

May

First Advisor

Lori A. Clarke

Second Advisor

George S. Avrunin

Third Advisor

Leon J. Osterweil

Fourth Advisor

Maciej J. Ciesielski

Subject Categories

Software Engineering

Abstract

Human-intensive systems (HISs), where humans must coordinate with each other along with software and/or hardware components to achieve system missions, are increasingly prevalent in safety-critical domains (e.g., healthcare). Such systems are often complex, involving aspects such as concurrency and exceptional situations. For these systems, it is often difficult but important to determine requirements for the individual components that are necessary to ensure the system requirements are satisfied. In this thesis, we investigated an approach that employs interface synthesis methods developed for software systems to automatically derive such requirements for components used in HISs.

In previous work, we investigated a requirement deriver that employs a regular language learning algorithm to iteratively refine the derived requirement based on counterexamples generated by model checking techniques. Since this learning-based requirement deriver often did not scale well, we investigated several learning and model checking optimizations. These optimizations significantly improved performance but affected the counterexample generation heuristics, often widely varying the permissiveness of the derived requirements. For comparison purposes, we investigated a direct requirement deriver that was purported to have poor performance but guarantees the derived requirements are adequately permissive, conceptually meaning the requirements are permissive as possible without violating the system requirements. For our evaluation, we applied these requirement derivers to case studies in two important domains, healthcare and election administration.

Based on this evaluation, the direct requirement deriver with all optimizations applied had reasonable performance and ensures the derived requirements are adequately permissive. For the learning-based requirement deriver, many of the optimizations and heuristics have been presented previously, but we recommend how to selectively combine them to obtain reasonable performance while usually producing the adequately permissive derived requirements.

Since such derived requirements often reflect the system complexity, these requirements can be easily misunderstood. Thus, we also investigated building views of the requirements that abstract away or highlight certain aspects to try to improve their understandability. Each single view appears to improve understandability and the multiple views seem to complement each other further improving understandability. Such derived requirements and their views can be used to safely develop and deploy the components used in HISs.

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