Research Track session

This paper expands staleness-based memory leak detection by presenting a machine learning-based framework. The proposed framework is based on an idea that object staleness can be better leveraged in regard to similarity of objects; i.e., an object is more likely to have leaked if it shows significantly high staleness not observed from other similar objects with the same allocation context. A central part of the proposed framework is the modeling of heap objects. To this end, the framework observes the staleness of objects during a representative run of an application. From the observed data, the framework generates training examples, which also contain instances of hypothetical leaks. Via machine learning, the proposed framework replaces the error-prone user-definable staleness predicates used in previous research with a model-based prediction. The framework was tested using both synthetic and real-world examples. Evaluation with synthetic leakage workloads of SPEC2006 benchmarks shows that the proposed method achieves the optimal accuracy permitted by staleness-based leak detection. Moreover, by incorporating allocation context into the model, the proposed method achieves higher accuracy than is possible with object staleness alone. Evaluation with real-world memory leaks demonstrates that the proposed method is effective for detecting previously reported bugs with high accuracy.

This paper presents Sniper, an automated memory leak detection tool for C/C++ production software. To track the staleness of allocated memory (which is a clue to potential leaks) with little overhead (mostly <3%), Sniper leverages instruction sampling using performance monitoring units available in commodity processors. It also offloads the time- and space-consuming analyses, and works on the original software without modifying the underlying memory allocator; it neither perturbs the application execution nor increases the heap size. The Sniper can even deal with multithreaded applications with very low overhead. In particular, it performs a statistical analysis, which views memory leaks as anomalies, for automated and systematic leak determination. Consequently, it accurately detected real-world memory leaks with no false positive, and achieved an F-measure of 81% on average for 17 benchmarks stress-tested with various memory leaks.

JavaScript is used in web applications for achieving rich user interfaces and implementing core functionality. Unfortunately, JavaScript code is known to be prone to faults. In an earlier study, we found that over 65% of such faults are caused by the interaction of JavaScript code with the DOM at runtime (DOM-related faults). In this paper, we first perform an analysis of 190 bug reports to understand fixes commonly applied by programmers to these DOM-related faults; we observe that parameter replacements and DOM element validations are common fix categories. Based on these findings, we propose an automated technique and tool, called Vejovis, for suggesting repairs for DOM-based JavaScript faults. To evaluate Vejovis, we conduct a case study in which we subject Vejovis to 22 real-world bugs across 11 applications. We find that Vejovis accurately suggests repairs for 20 out of the 22 bugs, and in 13 of the 20 cases, the correct fix was the top ranked one.

Spreadsheets are widely used by end users for numerical computation in their business. Spreadsheet cells whose computation is subject to the same semantics are often clustered in a row or column. When a spreadsheet evolves, these cell clusters can degenerate due to ad hoc modifications or undisciplined copy-and-pastes. Such degenerated clusters no longer keep cells prescribing the same computational semantics, and are said to exhibit ambiguous computation smells. Our empirical study finds that such smells are common and likely harmful. We propose AmCheck, a novel technique that automatically detects and repairs ambiguous computation smells by recovering their intended computational semantics. A case study using AmCheck suggests that it is useful for discovering and repairing real spreadsheet problems.