Abstract. Object-oriented programs often require large heaps to run properly or meet performance goals. They use high-overhead collections, bulky data models, and large caches. Discovering this is quite challenging. Manual browsing and flat summaries do not scale to complex graphs with 20 million objects. Context is crucial to understanding responsibility and inefficient object connectivity. We summarize memory footprint with help from the dominator relation. Each dominator tree captures unique ownership. Edges between trees capture responsibility. We introduce a set of ownership structures, and quantify their abundance. We aggregate these structures, and use thresholds to identify important aggregates. We introduce the ownership graph to summarize responsibility, and backbone equivalence to aggregate patterns within trees. Our implementation quickly generates concise summaries. In two minutes, it generates a 14-node ownership graph from 29 million objects. Backbone equivalence identifies a handful of patterns that account for 80 % of a tree’s footprint. 1

A developer often needs to understand both the code structure and the execution structure of an object-oriented program. Class diagrams extracted from source are often sufficient to understand the code structure. However, existing static or dynamic analyses that produce raw graphs of objects and relations between them, do not convey design intent or readily scale to large programs. Imposing an ownership hierarchy on a program’s execution structure through ownership domain annotations provides an intuitive and appealing mechanism to obtain, at compile-time, a visualization of a system’s execution structure. The visualization conveys design intent, is hierarchical, and thus is more scalable than existing approaches that produce raw object graphs. We first describe the construction of the visualization and then evaluate it on two real Java programs of 15,000 lines of code each that have been previously annotated. In both cases, the automatically generated visualization fit on one page, and gave us insights into the execution structure that would be otherwise hard to obtain by looking at the code, at existing class diagrams, or at unreadable visualizations produced by existing compile-time approaches. 1.

Abstract. Object models capture key properties of object-oriented architectures, and they can highlight relationships between types, occurrences of sharing, and object encapsulation. We present a dynamic analysis to extract object models from legacy code bases. Our analysis reconstructs each intermediate heap from a log of object allocations and field writes, applies a sequence of abstraction-based operations to each heap, and combines the results into a single object model that conservatively approximates all observed heaps from the program’s execution. The resulting object models reflect many interesting and useful architectural properties. 1

We introduce object ownership profiling, a technique for finding and fixing memory leaks in object-oriented programs. Object ownership profiling is the first memory profiling technique that reports both a hierarchy of allocated objects along with size and time information aggregated up that hierarchy. In addition, it reveals the cross-hierarchy interactions that are essential to pinpointing the source of a leak. We identify five memory management anti-patterns, including two that involve rich heap structure and object interactions, and are novel contributions of this work. We apply object ownership profiling to find and fix memory leaks in the Alloy IDE v3 that users had complained about for years, and that had eluded detection by code reviews and type-based commercial memory profilers.

SharC is a recently developed system for checking data-sharing in multithreaded programs. Programmers specify sharing rules (readonly, protected by a lock, etc.) for individual objects, and the SharC compiler enforces these rules using static and dynamic checks. Violations of these rules indicate unintended data sharing, which is the underlying cause of harmful data-races. Additionally, SharC allows programmers to change the sharing rules for a specific object using a sharing cast, to capture the fact that sharing rules for an object often change during the object’s lifetime. SharC was successfully applied to a number of multi-threaded C programs. However, many programs are not readily checkable using SharC because their sharing rules, and changes to sharing rules, effectively apply to whole data structures rather than to individual objects. We have developed a system called Shoal to address this shortcoming.

For many object-oriented systems, it is often useful to have a runtime architecture that shows networks of communicating objects. But it is hard to statically extract runtime object graphs that provide architectural abstraction from existing programs written in general purpose languages, and that follow common design idioms. Previous approaches extract low-level non-hierarchical object graphs that do not provide architectural abstraction, change the language too radically for many existing implementations, or use a dynamic analysis. Static analysis, which takes all possible executions into account, is essential to extract a sound architecture, one that reveals all objects and relations that could possibly exist at runtime. Ownership domain type annotations specify in code architectural intent related to object encapsulation and communication. We propose a static analysis that leverages such types and extracts a hierarchical approximation of all possible runtime object graphs. The representation provides architectural abstraction, first by ownership hierarchy, and then by types. We proved core soundness results for the technique and evaluated it on 68 KLOC of real code.

Understanding the data structures in a program is crucial to understanding how the program works, or why it doesn’t work. Inspecting the code that implements the data structures, however, is an arduous task and often fails to yield insights into the global organization of a program’s data. Inspecting the actual contents of the heap solves these problems but presents a significant challenge of its own: finding an effective way to present the enormous number of objects it contains. In this paper we present Heapviz, a tool for visualizing andexploringsnapshotsoftheheapobtainedfromarunning Java program. Unlike existing tools, such as traditional debuggers, Heapviz presents a global view of the program state asagraph, togetherwithpowerfulinteractivecapabilitiesfor navigating it. Our tool employs several key techniques that help manage the scale of the data. First, we reduce the size and complexity of the graph by using algorithms inspired by static shape analysis to aggregate the nodes that make up a data structure. Second, we introduce a dominator-based layout scheme that emphasizes hierarchicalcontainmentand ownership relations. Finally, the interactive interface allows the user to expand and contract regions of the heap to modulate data structure detail, inspect individual objects and field values, and search for objects based on type or connectivity. By applying Heapviz to both constructed and real-world examples, we show that Heapviz provides programmers with a powerful and intuitive tool for exploring program behavior. Categories andSubjectDescriptors

Abstract. Flexible ownership domain annotations can express and enforce design intent related to encapsulation and communication in real-world object-oriented programs. Ownership domain annotations also provide an intuitive and appealing mechanism to obtain a sound visualization of a system’s execution structure at compile time. The visualization provides design intent, is hierarchical, and thus more scalable than existing approaches that produce mostly nonhierarchical raw object graphs. The research proposal is to make the ownership domains type system more flexible and develop the theory and the tools to produce a sound visualization of the execution structure from an annotated program and infer many of these annotations semi-automatically at compile time. 1 Problem Description To correctly modify an object-oriented program, a developer often needs to understand both the code structure (static hierarchies of classes) and the execution

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Object ownership is useful for many applications such as program verification, thread synchronization, and memory management. However, even lightweight ownership type systems impose considerable annotation overhead, which hampers their widespread application. This paper address this issue by presenting a tunable static type inference for Universe types. In contrast to classical type systems, ownership types have no single most general typing. Therefore, our inference is tunable: users can indicate a preference for certain typings by configuring heuristics through weights. A particularly effective way of tuning the static inference is to obtain these weights automatically through runtime ownership inference. We present how the constraints of the Universe type system can be encoded as a boolean satisfiability (SAT) problem, how the runtime ownership inference produces weights from program executions, and how a weighted Max-SAT solver finds a correct Universe typing that optimizes the weights. Our implementation provides the static and runtime inference engines, as well as a visualization tool.