4,951 papers · page 38 of 248
Deep neural networks (DNN) have achieved tremendous development in the past decade. While many DNN-driven software applications have been deployed to solve various tasks, they could also produce incorrect behaviors and result in massive losses. To reveal the incorrect behaviors a…
Deep learning has been increasingly adopted in many application areas. To construct valid deep learning models, developers must conform to certain computational constraints by carefully selecting appropriate neural architectures and hyperparameter values. For example, the kernel …
With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory…
Traditional public blockchain systems typically had very limited transaction throughput because of the bottleneck of the consensus protocol itself. With recent advances in consensus technology, the performance limit has been greatly lifted, typically to thousands of transactions …
The use of Artificial Intelligence (ai), and more specifically of Deep Learning (dl), in modern software systems, is nowadays widespread and continues to grow. At the same time, its usage is energy demanding and contributes to the increased CO2 emissions, and has a great financia…
Chemical reaction networks (CRNs) are an emerging distributed computational paradigm where programs are encoded as a set of abstract chemical reactions. CRNs can be compiled into DNA strands which perform the computations in vitro, creating a foundation for intelligent nanodevice…
Automatic program repair (APR) regularly faces the challenge of overfitting patches --- patches that pass the test suite, but do not actually address the problems when evaluated manually. Currently, overfit detection requires manual inspection or an oracle making quality control …
Massive data from software repositories and collaboration tools are widely used to study social aspects in software development. One question that several recent works have addressed is how a software project's size and structure influence team productivity, a question famously c…
We present the design and implementation of GraphFuzz, a new structure-, coverage- and object lifetime-aware fuzzer capable of automatically testing low-level Library APIs. Unlike other fuzzers, GraphFuzz models sequences of executed functions as a dataflow graph, thus enabling i…
Effective leadership is one of the key drivers of business and project success, and one of the most active areas of management research. But how does leadership work in agile software development, which emphasizes self-management and self-organization and marginalizes traditional…
Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can cause severe consequences. Unfortunately, e…
The static detection of divide-by-zero, a common programming error, is particularly prone to false positives because conventional static analysis reports a divide-by-zero bug whenever it cannot prove the safety property --- the divisor variable is not zero in all executions. When…
In order to support a variety of missions and deal with different flight environments, drone control programs typically provide configurable control parameters. However, such a flexibility introduces vulnerabilities. One such vulnerability, referred to as range specification bugs…
Fuzz testing operating system kernels remains a daunting task to date. One known challenge is that much of the kernel code is locked under specific kernel states and current kernel fuzzers are not effective in exploring such an enormous state space. We refer to this problem as th…
With the recent advances of Deep Neural Networks (DNNs) in real-world applications, such as Automated Driving Systems (ADS) for self-driving cars, ensuring the reliability and safety of such DNN-enabled Systems emerges as a fundamental topic in software testing. One of the essent…
Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determining the robustness of a DNN with respect to its energy consumption (energy robustness) is relatively …
Formal methods provide very powerful tools and techniques for the design and analysis of complex systems. Their practical application remains however limited, due to the widely accepted belief that formal methods require extensive expertise and a steep learning curve. Writing cor…
Automatic configuration tuning helps users who intend to improve software performance. However, the auto-tuners are limited by the huge configuration search space. More importantly, they focus only on performance improvement while being unaware of other important user intentions …
Diagnosing a performance bug triggered in production cloud environments is notoriously challenging. Extracting performance bug signatures can help cloud operators quickly pinpoint the problem and avoid repeating manual efforts for diagnosing similar performance bugs. In this pape…
Android is the most popular smartphone platform with over 85% market share. Its success is built on openness, and phone vendors can utilize the Android source code to make products with unique software/hardware features. On the other hand, the fragmentation and customization of A…