Optimizing Tradeoffs of Non-Functional Properties in Software

Software systems have become integral to the daily life of millions of people. These systems provide much of our entertainment (e.g., video games, feature-length movies, and YouTube) and our transportation (e.g., planes, trains and automobiles). They ensure that the electricity to power homes and businesses is delivered and are significant consumers of that electricity themselves. With so many people consuming software, the best balance between runtime, energy or battery use, and accuracy is different for some users than for others. With so many applications playing so many different roles and so many developers producing and modifying them, the tradeoff between maintainability and other properties must be managed as well.

Existing methodologies for managing these “non-functional” properties require significant additional effort. Some techniques impose restrictions on how software may be designed or require time-consuming manual reviews. These techniques are frequently specific to a single application domain, programming language, or architecture, and are primarily applicable during initial software design and development. Further, modifying one property, such as runtime, often changes another property as well, such as maintainability.

In this dissertation, we present a framework, exemplified by three case studies, for automatically manipulating interconnected program properties to find the optimal tradeoffs. We exploit evolutionary search to explore the complex interactions of diverse properties and present the results to users. We demonstrate the applicability and effectiveness of this approach in three application domains, involving different combinations of dynamic properties (how the program behaves as it runs) and static properties (what the source code itself is like). In doing so, we describe the ways in which those domains impact the choices of how to represent programs, how to measure their properties effectively, and how to search for the best among many candidate program implementations. We show that effective choices enable the framework to take unmodified human-written programs and automatically produce new implementations with better properties—and better tradeoffs between properties—than before.