Automatic Removal of Array Memory Leaks in Java

Shaham, Ran; Kolodner, Elliot K.; Sagiv, Shmuel

doi:10.1007/3-540-46423-9_4

Cited by 43 publications

(65 citation statements)

References 12 publications

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“…TVPI polyhedra form a so-called weakly relational domain and thereby constitute a proper subclass of general polyhedra. Other sub-classes include Difference Bounds Matrices (DBMs for short) [5,61,46], the Octagon domain [47] and the Octahedron domain [17]. The abstract domain of DBMs represents inequalities of the form x i −x j ≤ c ij , x i , x j ∈ X by storing c ij in an n×n matrix such that the entry at position i, j is c ij .…”

Section: Related Workmentioning

confidence: 99%

“…More generally, TVPI inequalities with coefficients -1,0 or 1 were proposed for constraint logic programming [34,38] and were subsequently generalised to the Octagon abstract domain [47] for the purpose of verifying C code. Elsewhere, Octagons have been used to detect memory leaks in Java programs [61]. Two variable inequalities were relaxed to arbitrary coefficients in the context of satisfiability testing in theorem proving [50,63].…”

Section: Related Workmentioning

confidence: 99%

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The two variable per inequality abstract domain

Simon

King

2010

Higher-Order Symb Comput

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Abstract. This article presents the Two Variable Per Inequality abstract domain (TVPI domain for short). This so-called weakly-relational domain is able to express systems of linear inequalities where each inequality has at most two variables. The domain represents a sweet-point in the performance-cost tradeoff between the faster Octagon domain and the more expressive domain of general convex polyhedra. In particular, we detail techniques to closely approximate integral TVPI systems, thereby finessing the problem of excessively growing coefficients, yielding -to our knowledge -the only relational domain that combines linear relations with arbitrary coefficients and strongly polynomial performance. Keywords: polyhedral analysis, integer programming, abstract interpretationStatic analysis methods have evolved from inferring prerequisite invariants for compiler optimisations to tools in their own right that are able to prove the absence of run-time errors of software. The abstract interpretation framework [21] provides a way of constructing and justifying program analyses. The key idea is to simulate each operation in a program with an abstract analog that operates on a description of the program state, rather than the state itself. The descriptions are chosen to trace a property of interest, for instance, an interval that encloses all possible values of a variable would be of value when reasoning about array bounds. These descriptions constitute what is known as an abstract domain. An abstract domain is usually presented as a lattice D, , , and the analysis itself is formulated as a set of recursive equations over D. The recursive equations are solved iteratively, until a fixpoint is reached which is detected using the ordering predicate . Each equation expresses how a program statement transforms the state at one program point to the state at another. Equations may be recursive because of loops in the program. The meet and join † This paper is a revised extract of the first author's PhD thesis [65], which, in turn, extends [72].

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The two variable per inequality abstract domain

Simon

King

2010

Higher-Order Symb Comput

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“…Compile-time static analysis can find double free and missing frees [20] and is complimentary to our work. Offline diagnostic tools accurately detect leaks using a combination of heap differencing [15,16,30] and fined-grained allocation/usage tracking [13,19,35,36,40,41]. These approaches are expensive and often require multiple executions and/or separate analysis to generate complex reports full of low-level details about individual objects.…”

Section: Related Workmentioning

confidence: 99%

“…Previous approaches for finding leaks use heap diagnosis tools that rely on a combination of heap differencing [15,16,30] and allocation and/or fine-grain object tracking [10,13,14,19,29,35,36,40,41]. These techniques can degrade performance by a factor of two or more, incur substantial memory overheads, rely on multiple executions, and/or offload work to a separate processor.…”

Section: Introductionmentioning

confidence: 99%

Detecting memory leaks in managed languages with Cork

Jump

McKinley

2009

Softw Pract Exp

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SUMMARYA memory leak in a managed program occurs when the program inadvertently maintains references to objects that it no longer needs. Memory leaks cause systematic heap growth which degrades performance and results in program crashes after perhaps days or weeks of execution. Prior approaches for detecting memory leaks rely on heap differencing or detailed object statistics which store state proportional to the number of objects in the heap. These overheads preclude their use on the same processor for deployed long-running applications. This paper introduces Cork as a tool that accurately identifies heap growth caused by leaks. It is space efficient (adding less than 1% to the heap) and time efficient (adding 2.3% on average to total execution time). We implement this approach of examining and summarizing the class of live objects during garbage collection in a class points-from graph (CPFG). Each node in the CPFG represents a class and and edges between nodes represent references between objects of the specific classes. Cork annotates nodes and edges with the corresponding volume of live objects. Cork identifies growing data structures across multiple collections and computes a class slice to identify leaks for the user. We experiment with two functions for identifying growth and show that Cork is accurate: it identifies systematic heap growth with no false positives in 4 of 15 benchmarks we tested. Cork's slice report enabled us to quickly identify and eliminate growing data structures in large and unfamiliar programs, something their developers had not previously done.

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“…If an application leaks memory, it first slows down the system in which it is running and eventually causes the system to run out of memory. Many memory-leak bugs have been reported (e.g., bug #4177795 in the Java Developer's Connection [13]) and experiments have shown that on average 39% of space could be saved by freeing reachable but unneeded objects [24,22].…”

Section: Introductionmentioning

confidence: 99%