Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to ensure that changes to existing software do not break the software. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant as the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. In this paper, we present a new greedy heuristic algorithm for selecting a minimal subset of a test suite T that covers all the requirements covered by T . We show how our algorithm was inspired by the concept analysis framework. We conducted experiments to measure the extent of test suite reduction obtained by our algorithm and prior heuristics for test suite minimization. In our experiments, our algorithm always selected same size or smaller size test suite than that selected by prior heuristics and had comparable time performance.

Debugging multithreaded programs, which involves detection and identification of the cause of data races, has proved to be a hard problem. Although there has been significant amount of research [20,28,25,12,10] on this topic, prior works rely on one important assumption -the debuggers must be aware of all the synchronization operations that take place during a program run. This assumption is a significant limitation as multithreaded programs, including the popular SPLASH-2 benchmark [30], have barriers and flag synchronizations implemented in the user code. We show that the lack of knowledge of these synchronization operations leads to unnecessary reporting of numerous races. Our experiments with SPLASH-2 benchmark suite show that 12-131 distinct segments in source code, on an average, give rise to well over 4 million dynamic instances of falsely reported races for these programs. We propose a dynamic software technique that identifies the user defined synchronizations exercised during a program run. This information not only helps avoids reporting of unnecessary races, but also helps a record/replay system to speedup the replay.Our evaluation confirms that our synchronization detector is highly accurate with no false negatives and very few false positives. Thus, reporting of nearly all unnecessary races is avoided. Finally, we show that the knowledge of synchronization operations resulted in about 23% reduction in replay time.

Fixing runtime bugs in long running programs using trace based analyses such as dynamic slicing was believed to be prohibitively expensive. In this paper, we present a novel execution fast forwarding technique that makes this feasible. While a naive solution is to divide the entire execution by checkpoints, and then apply dynamic slicing enabled by tracing to one checkpoint interval at a time, it is still too costly even with state-of-the-art tracing techniques. Our technique is derived from two key observations. The first one is that long running programs are usually driven by events, which has been taken advantage of by checkpointing/replaying techniques to deterministically replay an execution from the event log. The second observation is that all the events are not relevant to replaying a particular part of the execution, in which the programmer suspects an error happened. We develop a slicing-like technique that can be used to prune irrelevant events from the event log. Driven by the reduced log, the replayed execution is now traced for fault location. This replayed execution has the effect of fast forwarding, i.e the amount of executed instructions is significantly reduced without losing the accuracy of reproducing a failure. Our evaluation shows that skipping irrelevant events can reduce the space requirement for dynamic slicing by factors ranging from 72 to 44490. We also describe how checkpointing and tracing enabled dynamic slicing are combined, which we believe is the first attempt to integrate these two techniques. Finally, the dynamic slices of a set of reported bugs for long running programs are studied to show the effectiveness of dynamic slicing.

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Debugging long running multithreaded programs is a very challenging problem when using tracing-based analyses. Since such programs are non-deterministic, reproducing the bug is non-trivial and generating and inspecting traces for long running programs can be prohibitively expensive. We propose a framework in which, to overcome the problem of bug reproducibility, a lightweight logging technique is used to log the events during the original execution. When a bug is encountered, it is reproduced using the generated log and during the replay, a fine-grained tracing technique is employed to collect control-flow/dependence traces that are then used to locate the root cause of the bug. In this paper, we address the key challenges resulting due to tracing, that is, the prohibitively high expense of collecting traces and the significant burden on the user who must examine the large amount of trace information to locate the bug in a long-running multithreaded program. These challenges are addressed through execution reduction that realizes a combination of logging and tracing such that traces collected contain only the execution information from those regions of threads that are relevant to the fault. This approach is highly effective because we observe that for long running multithreaded programs, many threads that execute are irrelevant to the fault. Hence, these threads need not be replayed and traced when trying to reproduce the bug. We develop a novel lightweight scheme that identifies such threads by observing all the interthread data dependences and removes their execution footprint in the replay run. In addition, we identify regions of thread executions that need not be replayed or, if they must be replayed, we determine if they need not be traced. Following execution reduction, the replayed execution takes lesser time to run and it produces a much smaller trace than the original execution. Thus, the cost of collecting traces and the effort of examining the traces to locate the fault are greatly reduced.

Abstract

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to ensure that changes to existing software do not break the software. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant as the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. In this paper, we present a new greedy heuristic algorithm for selecting a minimal subset of a test suite T that covers all the requirements covered by T . We show how our algorithm was inspired by the concept analysis framework. We conducted experiments to measure the extent of test suite reduction obtained by our algorithm and prior heuristics for test suite minimization. In our experiments, our algorithm always selected same size or smaller size test suite than that selected by prior heuristics and had comparable time performance.