Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from change logs or statistical models of API usage.

This paper addresses a different API migration scenario where there is no prior knowledge of the target library. We have no historical changelogs and no access to its internal representation. To tackle this problem, this paper proposes a novel approach (M$^3$), where probabilistic program synthesis is used to semantically model the behavior of library functions. Then, we use an SMT-based code search engine to discover similar code in user applications. These discovered instances provide potential locations for API migrations.

We evaluate our approach against 7 well-known libraries from varied application domains, learning correct implementations for 94 functions. Our approach is integrated with standard compiler tooling, and we used this integration to evaluate migration opportunities in 9 existing C/C++ applications with over 1MLoC. We discover over 7,000 instances of these functions, of which more than 2,000 represent migration opportunities.

Bruce Collie

University of Edinburgh

Philip Ginsbach

GitHub Software UK

Jackson Woodruff

University of Edinburgh

Ajitha Rajan

University of Edinburgh

United Kingdom

Michael F. P. O'Boyle

University of Edinburgh

The increasing adoption of the Linux kernel has been sustained by a large and constant maintenance effort, performed by a wide and heterogeneous base of contributors. One important problem that maintainers face in any code base is the rapid understanding of complex data structures. The Linux kernel is written in the C language, which enables the definition of arbitrarily uninformative datatype, via the use of casts and pointer arithmetic, of which doubly linked lists are a prominent example. In this paper, we explore the advantages and disadvantages of such lists, for expressivity, for code understanding, and for code reliability. Based on our observations, we have developed a toolset that includes inference of descriptive list types and a tool for list visualization. Our tools identify more than 10.000 list fields and variables in recent Linux kernel releases and succeeds in typing more than 90%. We show how these tools could have been used to detect previously fixed bugs and identify 6 new ones.

Nic Volanschi

Inria Bordeaux

Julia Lawall

Inria

Developers continuously invent new practices, usually grounded in hard-won experience, not theory. Game theory studies cooperation and conflict; its use will speed the development of effective processes. A survey of game theory in software engineering finds highly idealised models that are rarely based on process data. This is because software processes are hard to analyse using traditional game theory since they generate huge game models. We are the first to show how to use game abstractions, developed in artificial intelligence, to produce tractable game-theoretic models of software practices. We present Game-Theoretic Process Improvement (GTPI), built on top of empirical game-theoretic analysis. Some teams fall into the habit of preferring “quick-and-dirty” code to slow-to-write, careful code, incurring technical debt. We showcase GTPI’s ability to diagnose and improve such a development process. Using GTPI, we discover a lightweight intervention that incentivises developers to write careful code: add a single code reviewer who needs to catch only 25% of kludges. This 25% accuracy is key; it means that a reviewer does not need to examine each commit in-depth, making this process intervention cost-effective.

Link to Publication: https://www.sciencedirect.com/science/article/pii/S0164121219301980

Carlos Gavidia-Calderon

University College London

Federica Sarro

University College London, UK

United Kingdom

Mark Harman

University College London, UK

United Kingdom

Earl T. Barr

University College London, UK