2,847 papers · page 24 of 143
Reverse engineers would acquire valuable insights from descriptive function names, which are absent in publicly released binaries. Recent advances in binary function name prediction using data-driven machine learning show promise. However, existing approaches encounter difficulti…
The emergence of the ‘code naturalness’ concept, which suggests that software code shares statistical properties with natural language, paves the way for deep neural networks (DNNs) in software engineering (SE). However, DNNs can be vulnerable to certain human imperceptible varia…
Patch robustness certification is an emerging kind of defense technique against adversarial patch attacks with provable guarantees. There are two research lines: certified recovery and certified detection. They aim to correctly label malicious samples with provable guarantees and…
Abstractive summarization (AS) systems, which aim to generate a text for summarizing crucial information of the original document, have been widely adopted in recent years. Unfortunately, factually unreliable summaries may still occur, leading to unexpected misunderstanding and d…
C++ is crucial in software development, providing low-level memory control for performance and supporting object-oriented programming to construct modular, reusable code structures. Consequently, tackling pointer analysis for C++ becomes challenging, given the need to address the…
Python projects grow quickly by code reuse and building automation based on third-party libraries. However, the version constraints associated with these libraries are prone to mal-configuration, and this forms a major obstacle to correct project building (known as dependency-con…
As one of the next frontiers of software engineering, agile development at scale has attracted more and more research interests and efforts. When following the existing autonomy-focused and goal-driven lessons and guidelines to scale agile development for a large astronomy projec…
Though many deep learning (DL)-based vulnerability detection approaches have been proposed and indeed achieved remarkable performance, they still have limitations in the generalization as well as the practical usage. More precisely, existing DL-based approaches (1) perform negati…
GPUs have cemented their position in computer systems, not restricted to graphics but also extensively used for general-purpose computing. With this comes a rapidly expanding population of developers using GPUs for programming. However, programming with GPUs is notoriously diffic…
Current function identification techniques have been mostly focused on a specific set of binaries compiled for a specific CPU architecture. While recent deep-learning-based approaches theoretically can handle binaries from different architectures, they require significant computa…
Android fragmentation refers to the increasing variety of Android devices and operating system versions. Their number make it impossible to test an app on every supported device, resulting in many device compatibility issues and leading to poor user experiences. To mitigate this,…
Maintaining a good performance of a software system is a primordial task when evolving a software system. The performance regression issues are among the dominant problems that large software systems face. In addition, these large systems tend to be highly configurable, which all…
Library migration, which replaces the current library with a different one to retain the same software behavior, is common in software evolution. An essential part of this is finding an analogous API for the desired functionality. However, due to the multitude of libraries/APIs, …
Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a bug fix or a new feature) is being propagated, timely and without errors, to implementations in oth…
During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Models (LLMs) have been shown to be helpful “copilots” in assisting developers with various coding tas…
Deep Learning (DL) is prevalently used in various industries to improve decision-making and automate processes, driven by the ever-evolving DL libraries and compilers. The correctness of DL systems is crucial for trust in DL applications. As such, the recent wave of research has …
Dynamic taint analysis - a program analysis technique that checks whether information flows between particular source and sink locations in the program, has numerous applications in security, program comprehension, and software testing. Specifically, in mobile software, taint ana…
Enlightened by the big success of pre-training in natural language processing, pre-trained models for programming languages have been widely used to promote code intelligence in recent years. In particular, BERT has been used for bug localization tasks and impressive results have…
Given a target program state (or statement) s, what is the probability that an input reaches s? This is the quantitative reachability analysis problem. For instance, quantitative reachability analysis can be used to approximate the reliability of a program (where s is a bad state…
Cloud services are omnipresent and critical cloud service failure is a fact of life. In order to retain customers and prevent revenue loss, it is important to provide high reliability guarantees for these services. One way to do this is by predicting outages in advance, which can…