Melton： a practical and precise memory leak detection tool for C programs

Memory leaks are a common type of defect that is hard to detect manually. Existing memory leak detection tools suffer from lack of precise interprocedural analysis and path-sensitivity. To address this problem, we present a static interprocedural analysis algorithm, that performs fully pathsensitive analysis and captures precise function behaviors, to detect memory leak in C programs. The proposed algorithm uses path-sensitive symbolic execution to track memory actions in different program paths guarded by path conditions. A novel analysis model called memory state transition graph （MSTG） is proposed to describe the tracking process and its results. In order to do interprocedural analysis, the proposed algorithm generates a summary for each procedure from MSTG and applies the summary at the procedure＇s call sites. A prototype tool called Melton is implemented for this procedure. Melton was applied to five open source C programs and 41 leaks were found. More than 90% of these leaks were subsequently confirmed and fixed by their maintainers. For comparison with other tools, Melton was also applied to some programs in standard performance evaluation corporation （SPEC） CPU 2000 benchmark suite and detected more leaks than the state of the art approaches.

Demand-Driven Memory Leak Detection Based on Flow-and Context-Sensitive Pointer Analysis

We present a demand-driven approach to memory leak detection algorithm based on flow- and context-sensitive pointer analysis. The detection algorithm firstly assumes the presence of a memory leak at some program point and then runs a backward analysis to see if this assumption can be disproved. Our algorithm computes the memory abstraction of programs based on points-to graph resulting from flow- and context-sensitive pointer analysis. We have implemented the algorithm in the SUIF2 compiler infrastructure and used the implementation to analyze a set of C benchmark programs. The experimental results show that the approach has better precision with satisfied scalability as expected.

Prioritizing Test Cases for Memory Leaks in Android Applications

Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Although a large body of research has been devoted into the memory leak diagnosing techniques after leaks have been discovered, it is still challenging to find out the memory leak phenomena at first. Testing is the most widely used technique for failure discovery. However, traditional testing techniques are not directed for the discovery of memory leaks. They may spend lots of time on testing unlikely leaking executions and therefore can be inefficient. To address the problem, we propose a novel approach to prioritize test cases according to their likelihood to cause memory leaks in a given test suite. It firstly builds a prediction model to determine whether each test can potentially lead to memory leaks based on machine learning on selected code features. Then, for each input test case, we partly run it to get its code features and predict its likelihood to cause leaks. The most suspicious test cases will be suggested to run at first in order to reveal memory leak faults as soon as possible. Experimental evaluation on several Android applications shows that our approach is effective.

A Memory-Related Vulnerability Detection Approach Based on Vulnerability Features

Developing secure software systems is a major challenge in the software industry due to errors or weaknesses that bring vulnerabilities to the software system.To address this challenge,researchers often use the source code features of vulnerabilities to improve vulnerability detection.Notwithstanding the success achieved by these techniques,the existing studies mainly focus on the conceptual description without an accurate definition of vulnerability features.In this study,we introduce a novel and efficient Memory-Related Vulnerability Detection Approach using Vulnerability Features (MRVDAVF).Our framework uses three distinct strategies to improve vulnerability detection.In the first stage,we introduce an improved Control Flow Graph (CFG) and Pointer-related Control Flow Graph (PCFG) to describe the features of some common vulnerabilities,including memory leak,doublefree,and use-after-free.Afterward,two algorithms,namely Vulnerability Judging algorithm based on Vulnerability Feature (VJVF) and Feature Judging (FJ) algorithm,are employed to detect memory-related vulnerabilities.Finally,the proposed model is validated using three test cases obtained from Juliet Test Suite.The experimental results show that the proposed approach is feasible and effective.