We describe a system that simplifies the process of debugging programs produced by computer-aided parallelization tools. The system uses relative debugging techniques to compare serial and parallel executions in order to show where the computations begin to differ. If the original serial code is correct, errors due to parallelization will be isolated by the comparison.
One of the primary goals of the system is to minimize the effort required of the user. To that end, the debugging system uses information produced by the parallelization tool to drive the comparison process. In particular, the debugging system relies on the parallelization tool to provide information about where variables may have been modified and how arrays are distributed across multiple processes. User effort is also reduced through the use of dynamic instrumentation. This allows us to modify the program execution without changing the way the user builds the executable.
The use of dynamic instrumentation also permits us to compare the executions in a fine-grained fashion and only involve the debugger when a difference has been detected. This reduces the overhead of executing instrumentation.
Recent advances in program synthesis offer means to automatically debug student submissions and generate personalized feedback in massive programming classrooms. When automatically generating feedback for programming assignments, a key challenge is designing pedagogically useful hints that are as effective as the manual feedback given by teachers. Through an analysis of teachers' hint-giving practices in 132 online Q&A posts, we establish three design guidelines that an effective feedback design should follow. Based on these guidelines, we develop a feedback system that leverages both program …
MPJ Express is a messaging system that allows computational scientists to write and execute parallel Java applications on High Performance Computing (HPC) hardware. Despite its successful adoption in the Java HPC community, the MPJ Express software currently does not provide any support for debugging and profiling parallel applications and hence forces its users to rely on manual and tedious debugging/profiling methods. Support for such tools is essential to help application developers increase their overall productivity. To address this we have developed debugging and profiling tools for MPJ …
A number of automated techniques and tools were proposed in the research literature over the years which aim to support the spreadsheet developer in the process of testing and debugging a faulty spreadsheet. One underlying assumption of many of these approaches is that the spreadsheet developer is capable of providing test cases or is at least reliably able to determine whether a calculated value in a certain cell is correct given the current set of inputs. Since real-world spreadsheets can be complex, we argue that these assumptions might be too strong in some situations. We therefore propose…
Bugs in learners' programs are often the result of fundamental misconceptions. Teachers frequently face the challenge of first having to understand such bugs, and then suggest ways to fix them. In order to enable teachers to do so effectively and efficiently, it is desirable to support them in recognising and fixing bugs. Misconceptions often lead to recurring patterns of similar bugs, enabling automated tools to provide this support in terms of hints on occurrences of common bug patterns. In this paper, we investigate to what extent the hints improve the effectiveness and efficiency of teache…
Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, researchers have proposed various automated program repair (APR) techniques to reduce efforts of manual debugging. However, given a real-world buggy program, a typical APR technique usually generates a large number of patches, each of which needs to be validated against the original test suite which incurs extremely high computation costs. Although existing APR techniques have already leveraged various static and/or dynamic information to find the desired patches faster, they are…
University of Luxembourg（卢森堡大学）；University of Ottawa（渥太华大学）
We present HUDD, a tool that supports safety analysis practices for systems enabled by Deep Neural Networks (DNNs) by automatically identifying the root causes for DNN errors and retraining the DNN. HUDD stands for Heatmap-based Unsupervised Debugging of DNNs, it automatically clusters error-inducing images whose results are due to common subsets of DNN neurons. The intent is for the generated clusters to group error-inducing images having common characteristics, that is, having a common root cause. HUDD identifies root causes by applying a clustering algorithm to matrices (i.e., heatmaps) cap…
Various automated program repair (APR) techniques have been proposed to fix bugs automatically in the last decade. Although recent researches have made significant progress on the effectiveness and efficiency, it is still unclear how APR techniques perform with human intervention in a real debugging scenario. To bridge this gap, we conduct an extensive study to compare three state-of-the-art APR tools with manual program repair, and further investigate whether the assistance of APR tools (i.e., repair reports) can improve manual program repair. To that end, we recruit 20 participants for a con…
Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that DNN bug fix patterns are different from traditional bug fix patterns. Furthermore, those buggy models are non-trivial to diagnose and fix due to inexplicit errors with several options to fix them. To support developers in locating and fixing bugs, we propose DeepDiagnosis, a novel debugging approach that localizes the faults, reports error symptoms and suggests fixes for DNN programs. In the first phase, our techniq…
In introductory programming courses, it is challenging for instructors to provide debugging feedback on students' incorrect programs. Some recent tools automatically offer program repair feedback by identifying any differences between incorrect and correct programs, but suffer from issues related to scalability, accuracy, and cross-language portability. This paper presents FAPR -- our novel approach that suggests repairs based on program differences in a fast and accurate manner. FAPR is different from current tools in three aspects. First, it encodes syntactic information into token sequences…
Fault localization is one of the most time-consuming and error-prone parts of software debugging. There are several tools for helping developers in the fault localization process, however, they mostly target programs written in Java and C/C++ programming languages. While these tools are splendid on their own, we must not look over the fact that Python is a popular programming language, and still there are a lack of easy-to-use and handy fault localization tools for Python developers. In this paper, we present a tool called "CharmFL" for software fault localization as a plug-in for PyCharm IDE.…
