4,951 papers · page 39 of 248
Machine Vision Components (MVC) are becoming safety-critical. Assuring their quality, including safety, is essential for their successful deployment. Assurance relies on the availability of precisely specified and, ideally, machine-verifiable requirements. MVCs with state-of-the-…
Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a si…
Online technical forums (e.g., StackOverflow) are popular platforms for developers to discuss technical problems such as how to use a specific Application Programming Interface (API), how to solve the programming tasks, or how to fix bugs in their code. These discussions can ofte…
Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant drawbacks: grammar-based autocompletion is restricted in dynamically-typed language environm…
Large pre-trained language models such as GPT-3 [10], Codex [11], and Google's language model [7] are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism and caution. On the optimistic sid…
Software systems may contain critical program components such as patented program logic or sensitive data. When those components are reverse-engineered by adversaries, it can cause significantly damage (e.g., financial loss or operational failures). While protecting critical prog…
Automatic static analysis tools (ASATs), such as Findbugs, have a high false alarm rate. The large number of false alarms produced poses a barrier to adoption. Researchers have proposed the use of machine learning to prune false alarms and present only actionable warnings to deve…
Mutation testing offers concrete test goals (mutants) and a rigorous test efficacy criterion, but it is expensive due to vast numbers of mutants, many of which are neither useful nor actionable. Prior work has focused on selecting representative and sufficient mutant subsets, mea…
Developers frequently change the type of a program element and update all its references to increase performance, security, or maintainability. Manually performing type changes is tedious, error-prone, and it overwhelms developers. Researchers and tool builders have proposed adva…
Log message template identification aims to convert raw logs containing free-formed log messages into structured logs to be processed by automated log-based analysis, such as anomaly detection and model inference. While many techniques have been proposed in the literature, only t…
Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their codebases and find potential bugs. In order to m…
We present BayeSmith, a general framework for automatically learning probabilistic models of static analysis alarms. Several probabilistic reasoning techniques have recently been proposed which incorporate external feedback on semantic facts and thereby reduce the user's alarm in…
Deep learning (DL) has become an integral part of solutions to various important problems, which is why ensuring the quality of DL systems is essential. One of the challenges of achieving reliability and robustness of DL software is to ensure that algorithm implementations are nu…
Fuzz testing (fuzzing) allows developers to detect bugs and vulnerabilities in code by automatically generating defect-revealing inputs. Most fuzzers operate by generating inputs for applications and mutating the bytes of those inputs, guiding the fuzzing process with branch cove…
Software-intensive systems produce logs for troubleshooting purposes. Recently, many deep learning models have been proposed to automatically detect system anomalies based on log data. These models typically claim very high detection accuracy. For example, most models report an F…
We present NPEX, a new technique for repairing Java null pointer exceptions (NPEs) without tests. State-of-the-art NPE repair techniques rely on test suites written by developers for patch validation. Unfortunately, however, those are typically future test cases that are unavaila…
Most Android apps lack their counterparts on convenient smart-watch devices, possibly due to non-trivial engineering efforts required in the new app design and code development. Inspired by the observation that widgets on a smartphone can be mirrored to a smartwatch, this paper p…
The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. We present DEAR, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or multiple consecutive stateme…
Flaky tests are tests that pass or fail nondeterministically on the same version of code. These tests can mislead developers concerning the quality of their code changes during regression testing. A common kind of flaky tests are order-dependent tests, whose pass/fail outcomes de…
Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program synthesis. However, most existing methods generate neural program embeddings directly from the progr…