13 papers · ranked by Valyu relevance
Jiatong Wu, Sen Wang, Kai Niu, Yifei She + 2 more
Classical Algorithmic Information Theory (AIT) provides a rigorous foundation for information-based similarity measurement, but classical formulations and their compression-based approximations largely operate at the syntactic level, making them sensitive to surface-level variation and insufficient for semantic…
Ying Yin, Yuhai Zhao, Yiming Sun, Chen Chen + 1 more
At present, the explosive growth of software code volume and quantity makes the code review process very labor-intensive and time-consuming. An automated code review model can assist in improving the efficiency of the process. Tufano et al., designed two automated tasks to help improve the efficiency of code review…
Fahmi H. Quradaa, Sara Shahzad, Rashad Saeed, Mubarak M. Sufyan + 1 more
In software development, it’s common to reuse existing source code by copying and pasting, resulting in the proliferation of numerous code clones-similar or identical code fragments-that detrimentally affect software quality and maintainability. Although several techniques for code clone detection exist, many encounter…
Chengguan Xiang, Ying Wang, Qiyun Zhou, Zhen Yu
This paper proposes an algorithm for the automatic assessment of programming exercises. The algorithm assigns assessment scores based on the program dependency graph structure and the program semantic similarity, but does not actually need to run the student’s program. By calculating the node similarity between the…
Ana Rita Batista, Vasiliki Folia, Susana Silva, Hualou Liang
Background: Cognitive stimulation programs typically consist of task collections (“bundles”) designed to cover various aspects of a cognitive domain and/or sustain user engagement. However, task order is often overlooked, despite variations in difficulty based on structure or mode of implementation. This study examined…
Alasdair Armstrong, Brian Campbell, Ben Simner, Christopher Pulte + 1 more
Architecture specifications such as Armv8-A and RISC-V are the ultimate foundation for software verification and the correctness criteria for hardware verification. They should define the allowed sequential and relaxed-memory concurrency behaviour of programs, but hitherto there has been no integration of full-scale…
Vladimir E. Zyubin, Natalia O. Garanina, Igor S. Anureev, Sergey M. Staroletov + 3 more
'Sergey M. Staroletov' 'Andreas Komninos' 'Panagiotis Katsaros' 'Kleanthis Thramboulidis'] The paper proposes a topology-free specification of distributed control systems by means of a process-oriented programming paradigm. The proposed approach was characterized, on the one hand, by a topologically independent…
Parisa Kordjamshidi, Dan Roth, Kristian Kersting
Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models'…
Alessandro Abate, Haniel Barbosa, Clark Barrett, Cristina David + 5 more
'Pascal Kesseli' 'Daniel Kroening' 'Elizabeth Polgreen' 'Andrew Reynolds' 'Cesare Tinelli'] Program synthesis is the mechanised construction of software. One of the main difficulties is the efficient exploration of the very large solution space, and tools often require a user-provided syntactic restriction of the…
Sepideh Asadi, Martin Blicha, Antti E. J. Hyvärinen, Grigory Fedyukovich + 1 more
'Grigory Fedyukovich' 'Natasha Sharygina'] This article provides an innovative approach for verification by model checking of programs that undergo continuous changes. To tackle the problem of repeating the entire model checking for each new version of the program, our approach verifies programs incrementally. It…
Francisco Pinto-Santos, Carolina Zato, Héctor Quintián, Tian Cheng Li + 1 more
Software vulnerability analysis is critical for maintaining secure and reliable systems, yet traditional Deep Learning (DL) models often act as “black boxes,” lacking transparency and failing to leverage the explicit structural semantics of code. In this paper, we propose KG-HiAttention, a novel neuro-symbolic…
Joshua S. Rule, Steven T. Piantadosi, Andrew Cropper, Kevin Ellis + 2 more
Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning…
Sophie Lathouwers, Yujie Liu, Vadim Zaytsev
In software engineering, models are used for many different things. In this paper, we focus on program verification, where we use models to reason about the correctness of systems. There are many different types of program verification techniques which provide different correctness guarantees. We investigate the domain…