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We introduce CPP-UT-Bench, a benchmark dataset to measure C++ unit test generation capability of a large language model (LLM). CPP-UT-Bench aims to reflect a broad and diverse set of C++ codebases found in the real world. The dataset…

Computing-in-memory (CIM) promises to alleviate the Von Neumann bottleneck and accelerate data-intensive applications. Depending on the underlying technology and configuration, CIM enables implementing compute primitives in place, such as…

Cooperation between verification methods is crucial to tackle the challenging problem of software verification. The paper focuses on the verification of C programs using pointers and it formalizes a cooperation between static analyzers…

We present nanoBench, a tool for evaluating small microbenchmarks using hardware performance counters on Intel and AMD x86 systems. Most existing tools and libraries are intended to either benchmark entire programs, or program segments in…

Dynamic data race detectors are indispensable for flagging concurrency errors in software, but their high runtime overhead limits their adoption. This overhead stems primarily from pervasive instrumentation of memory accesses - a…

The vast number of interleavings that a concurrent program can have is typically identified as the root cause of the difficulty of automatic analysis of concurrent software. Weak memory is generally believed to make this problem even…

Software Defined Networking (SDN) has become a new paradigm in computer networking, introducing a decoupled architecture that separates the network into the data plane and the control plane. The control plane acts as the centralized brain,…

As concurrent programming becomes increasingly prevalent, effectively identifying and addressing concurrency issues such as data races and deadlocks is critical. This study evaluates the performance of several leading large language models…

Embedded systems are everywhere, from home appliances to critical systems such as medical devices. They usually have associated timing constraints that need to be verified for the implementation. Here, we use an untimed bounded model…

This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we…

Performance of clustering algorithms is evaluated with the help of accuracy metrics. There is a great diversity of clustering algorithms, which are key components of many data analysis and exploration systems. However, there exist only few…

Program executions under relaxed memory model (rmm) semantics are significantly more difficult to analyze; the rmm semantics result in out of order execution of program events leading to an explosion of state-space. Dynamic partial order…

Deep neural networks generate and process large volumes of data, posing challenges for low-resource embedded systems. In-memory computing has been demonstrated as an efficient computing infrastructure and shows promise for embedded AI…

Data race, a category of insidious software concurrency bugs, is often challenging and resource-intensive to detect and debug. Existing dynamic race detection tools incur significant execution time and memory overhead while exhibiting high…

Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect…

Test smells, similar to code smells, can negatively impact both the test code and the production code being tested. Despite extensive research on test smells in languages like Java, Scala, and Python, automated tools for detecting test…

Dynamic memory management requires special attention in programming. It should be fast and secure at the same time. This paper proposes a new randomized dynamic memory management algorithm designed to meet these requirements. Randomization…

We present a framework for efficient stateless model checking (SMC) of concurrent programs under five prominent models of causal consistency, CCv,CM,CC, Read Committed and Read Atomic. Our approach is based on exploring traces under the…

Recent advances in large language models (LLMs) have made it increasingly difficult to distinguish human-written text from AI-generated content. Many existing detectors train supervised neural classifiers that achieve strong in-distribution…

Machine learning and data analytics applications increasingly suffer from the high latency and energy consumption of conventional von Neumann architectures. Recently, several in-memory and near-memory systems have been proposed to remove…