Effective error explanation techniques for concurrent software

Zusammenfassung in deutscher SpracheConcurrency bugs are among the most difficult software bugs to detect and diagnose. This is mainly due to the inherent inability of humans to comprehend concurrently executing computations and to foresee the possible interleavings that they can entail. In concurrent programs, interactions between concurrent computations and the order of program events can vary across executions. This nondeterminism may result in erroneous and undesired program behavior whose root cause is difficult to analyze. Facing the challenge of debugging concurrent programs, this dissertation proposes effective concurrency bug explanation techniques to assist programmers in understanding the cause of failure in concurrent programs. Our techniques which do not rely on any characteristics specific to one type of bug, provide general frameworks for explaining bugs both in shared memory multithreaded programs and message passing concurrent systems. In devising our dynamic techniques for bug explanation, we follow two different approaches, namely anomaly detection and slicing. Our anomaly detection techniques are based on statistical analysis and pattern mining. These techniques adapt a standard pattern mining algorithm called sequential pattern mining to extract anomalies from datasets of failing and passing execution traces. Anomalies in the form of sequences of events reveal the problematic or unexpected order between concurrent events which may cause a failure. To address scalability issues of the standard sequential pattern mining algorithm in extracting anomalies, we propose three different approximation methods according to the problem setting. The first technique under-approximates anomalies by limiting them to sequences of events which occur consecutively in traces. The second technique makes the pattern mining problem more tractable by shortening the length of the traces via partitioning them into subtraces. The third technique is based on a novel abstraction technique for improving the scalability of the pattern mining algorithm. This technique reduces the length and the number of distinct events in traces while preserving the ordering information between the events of original traces including context switches which are crucial for understanding concurrency bugs. The bug patterns extracted by this technique not only reveal the problematic interleavings but also the context in which the bug occurred. In a completely different approach, we use a proof-based technique to construct semantics-aware slices from failing traces. This technique analyzes a single symbolic execution trace to isolate a slice comprising a code fragment and schedule causing a concurrency bug as well as annotations that describe the erroneous program states. Our slicing technique, in fact, generalizes existing interpolation-based frameworks for sequential bug explanation to a concurrency setting. We evaluate the efficiency and effectiveness of our proposed techniques on benchmarks covering a broad range of real-world concurrency bugs. Moreover, we compare the strengths and limitations of the anomaly detection techniques with the proof-based slicing technique.16

Abstraction and mining of traces to explain concurrency bugs

We propose an automated mining-based method for explaining concurrency bugs. We use a data mining technique called sequential pattern mining to identify problematic sequences of concurrent read and write accesses to the shared memory of a multithreaded program. Our technique does not rely on any characteristics specific to one type of concurrency bug, thus providing a general framework for concurrency bug explanation. In our method, given a set of concurrent execution traces, we first mine sequences that frequently occur in failing traces and then rank them based on the number of their occurrences in passing traces. We consider the highly ranked sequences of events that occur frequently only in failing traces an explanation of the system failure, as they can reveal its causes in the execution traces. Since the scalability of sequential pattern mining is limited by the length of the traces, we present an abstraction technique which shortens the traces at the cost of introducing spurious explanations. Spurious as well as misleading explanations are then eliminated by a subsequent filtering step, helping the programmer to focus on likely causes of the failure. We validate our approach using a number of case studies, including synthetic as well as real-world bugs.Austrian National Research Networ