Software-reliant systems need to evolve over time to meet new requirements and take advantage of new technology. However, all too often the structure of software becomes too complex to allow rapid and cost-effective improvements. This increasing complexity is often also a sign of degrading software architecture, making isolating a portion of software for use in a new context or for clean replacement by an improved version difficult. Isolating entangled software from the rest of the architecture typically relies on manual efforts to refactor code that can take thousands of days of effort, as tools provide only limited support for such activities. In this paper, we describe a search-based algorithm that recommends a series of refactorings that collectively isolate specified software from its tangle of architectural dependencies. This approach generates recommendations that reduce problematic dependencies by more than 87% on codebases as large as 1.2M LOC and has the potential to reduce the effort required for this kind of architecture improvement by two-thirds. In walkthroughs, developers found more than 84% of the recommended refactorings acceptable. Our approach provides a much needed foundation for tool support that addresses challenges commonly encountered when improving the architecture of existing software.

Software refactoring plays an important role in software engineering. Developers often turn to refactoring when they want to restructure software to improve its quality without changing its external behavior. Small-scale (floss) refactoring is common in industry and is often performed by a single developer in short sessions, even though developers do much of this work manually instead of using refactoring tools. However, some refactoring efforts are much larger in scale, requiring entire teams and months or years of effort, and the role of tools in these efforts is not as well studied. In this paper, we report on a survey we conducted with developers to understand large-scale refactoring and its tool support needs. Our results from 107 industry developers demonstrate that projects commonly go through multiple large-scale refactorings, each of which requires considerable effort. Our study finds that developers use several categories of tools to support large-scale refactoring and rely more heavily on general-purpose tools like IDEs than on tools designed specifically to support refactoring. Tool support varies across the different activities, with some particularly challenging activities seeing little use of tools in practice. Furthermore, our analysis suggests significant impact is possible through advances in tool support for comprehension and testing, as well as through support for the needs of business stakeholders.

Despite progress in providing software engineers with tools that automate an increasing number of development tasks, complex activities like redesigning and reengineering existing software remain resource intensive or are supported by tools that are error prone. Complex, but common tasks in industry, like evolving large codebases (1M+ SLOC) to meet changing needs, still rely on costly manual efforts and incur significant technical risk. In one example, an organization that we work with estimated 14,000 hours of development work alone (excluding integration and testing) to isolate a feature from the underlying hardware platform. These examples are pervasive in industry. Software engineering research has taken providing effective tools for software evolution for granted for far too long. The time is right for research to take advantage of advances in search-based software engineering and create the next generation of industry-relevant automated software evolution tools. This paper lays out a vision for automated refactoring at scale towards this goal.