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Register a new userFReD: Automated Debugging via Binary Search through a Process Lifetime
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Added by
Kapil Arya
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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Reversible debuggers have been developed at least since 1970. Such a feature is useful when the cause of a bug is close in time to the bug manifestation. When the cause is far back in time, one resorts to setting appropriate breakpoints in the debugger and beginning a new debugging session. For these cases when the cause of a bug is far in time from its manifestation, bug diagnosis requires a series of debugging sessions with which to narrow down the cause of the bug. For such difficult bugs, this work presents an automated tool to search through the process lifetime and locate the cause. As an example, the bug could be related to a program invariant failing. A binary search through the process lifetime suffices, since the invariant expression is true at the beginning of the program execution, and false when the bug is encountered. An algorithm for such a binary search is presented within the FReD (Fast Reversible Debugger) software. It is based on the ability to checkpoint, restart and deterministically replay the multiple processes of a debugging session. It is based on GDB (a debugger), DMTCP (for checkpoint-restart), and a custom deterministic record-replay plugin for DMTCP. FReD supports complex, real-world multithreaded programs, such as MySQL and Firefox. Further, the binary search is robust. It operates on multi-threaded programs, and takes advantage of multi-core architectures during replay.
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It is widely recognized that program repair tools need to have a high precision to be useful, i.e., the generated patches need to have a high probability to be correct. However, it is fundamentally difficult to ensure the correctness of the patches, and many tools compromise other aspects of repair performance such as recall for an acceptable precision. In this paper we ask a question: can a repair tool with a low precision be still useful? To explore this question, we propose an interactive filtering approach to patch review, which filters out incorrect patches by asking questions to the developers. Our intuition is that incorrect patches can still help understand the bug. With proper tool support, the benefit outweighs the cost even if there are many incorrect patches. We implemented the approach as an Eclipse plugin tool, InPaFer, and evaluated it with a simulated experiment and a user study with 30 developers. The results show that our approach improve the repair performance of developers, with 62.5% more successfully repaired bugs and 25.3% less debugging time in average. In particular, even if the generated patches are all incorrect, the performance of the developers would not be significantly reduced, and could be improved when some patches provide useful information for repairing, such as the faulty location and a partial fix.
JavaScript (JS) is a popular, platform-independent programming language. To ensure the interoperability of JS programs across different platforms, the implementation of a JS engine should conform to the ECMAScript standard. However, doing so is challenging as there are many subtle definitions of API behaviors, and the definitions keep evolving. We present COMFORT, a new compiler fuzzing framework for detecting JS engine bugs and behaviors that deviate from the ECMAScript standard. COMFORT leverages the recent advance in deep learning-based language models to automatically generate JS test code. As a departure from prior fuzzers, COMFORT utilizes the well-structured ECMAScript specifications to automatically generate test data along with the test programs to expose bugs that could be overlooked by the developers or manually written test cases. COMFORT then applies differential testing methodologies on the generated test cases to expose standard conformance bugs. We apply COMFORT to ten mainstream JS engines. In 200 hours of automated concurrent testing runs, we discover bugs in all tested JS engines. We had identified 158 unique JS engine bugs, of which 129 have been verified, and 115 have already been fixed by the developers. Furthermore, 21 of the Comfort-generated test cases have been added to Test262, the official ECMAScript conformance test suite.
UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the design space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks.
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Shiri Morshtein
, Shmuel Tyszberowicz (RISE - Centre for Research andn Innovation in Software Engineering
2021
Formal software verification techniques are widely used to specify and prove the functional correctness of programs. However, nonfunctional properties such as time complexity are usually carried out with pen and paper. Inefficient code in terms of time complexity may cause massive performance problems in large-scale complex systems. We present a proof of concept for using the Dafny verification tool to specify and verify the worst-case time complexity of binary search. This approach can also be used for academic purposes as a new way to teach algorithms and complexity.
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