Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their effects are often discussed without distinguishing between task types, developer seniority, and verification demands. This paper presents a mixed-method empirical study of LLM-assisted software engineering with first-year and fourth-year undergraduates. Phase 1 is a preliminary survey (n=157) that characterizes LLM exposure, reliance, and trust calibration among the two groups. Phase 2 is a task-based quasi-experiment with a purposive sample from both cohorts (n=20). Here, we compare AI-assisted and non-AI conditions on a structured set of software engineering tasks spanning implementation, constraint-driven algorithm selection, and architectural reasoning. We then analyze performance outcomes alongside behavioral traces captured via screen recording and a qualitative coding process. Survey results indicate widespread LLM adoption and substantial verification effort, alongside cohort differences in perceived LLM capability for constraint-heavy scenarios. The quasi-experiment further shows that AI assistance changes workflow structure. For example, participants frequently adopt AI-first task entry, copy-transfer integration, and AI-mediated debugging, whereas non-AI workflows rely more on documentation, prior templates, and iterative trial-error refinement. Overall, our findings suggest that the benefits of LLM assistance are task-dependent and mediated by expertise and verification practices, rather than by generation speed alone.

Given the widespread prevalence of vulnerabilities, remediation is a critical phase that every software project has to go through. When comparing the studies on understanding the security vulnerabilities in software, such as vulnerability discovery and patterns, there is a lack of studies on the vulnerability remediation phase. To address this, we have done a timeline analysis for 130 of the most dependent upon open source projects written in JavaScript language, hosted on GitHub to understand the nature and the lifetime of the vulnerabilities in those projects. We used a static code analyzer on 501K commits from the repositories to identify commits that introduced new vulnerabilities to the code and fixed existing vulnerabilities in the code. In 90% of the projects, we identified that a commit that fixed an existing vulnerability had introduced one or more new vulnerabilities into the code. On average, 16% of the commits intended to fix vulnerabilities have introduced one or more new vulnerabilities from the analyzed projects. We also found that 18% of the total vulnerabilities found in those projects have originated from a commit meant to fix an existing vulnerability, and 78% of those vulnerabilities could have been avoided of introduction if the developers were to use proper internal testing. Here, we demonstrate Sequza, a visualization tool to help organizations detect such instances at the earliest possible.

While there is a large body of work on understanding vulnerabilities in the wild, little has been done to understand the dynamics of the remediation phase of the development cycle. To this end, we have done a timeline analysis on 118K commits from 53 of the most used JavaScript projects from GitHub to understand the provenance and prevalence of vulnerabilities in those projects. We used a vulnerability detector (CodeQL) to filter commits that introduced vulnerabilities and the commits that fixed a prior vulnerability. We found that in 82% of the projects, a commit fixing a prior vulnerability, in turn, introduced one or more new vulnerabilities. Among those projects, on average, 18% of the commits intended to fix vulnerabilities, in turn, introduced one or more new vulnerabilities. We also found that 50% of the total vulnerabilities found in those projects originated from a commit meant to fix a prior vulnerability, and 78% of those vulnerabilities could have been avoided if they were to use proper internal testing. We provide critical insights into how proper internal testing can avoid a significant portion of vulnerabilities, increasing organizations' security posture.

A significant number of human and elephant lives have been lost due to the human-elephant conflict in Sri Lanka. To save lives of humans and elephants, it is therefore important to minimize encounters between them. In this paper, we present Eloc, a system that detects the presence of elephants using their infrasonic emissions near human habitats and then localize their positions. The high cost of infrasonic detectors is an important challenge to the real-world deployment of such localization systems, in particular in developing countries where the human-elephant conflict occurs. In order to address this problem, we design a low cost infrasonic detector that can be easily built using commodity off-the-shelf hardware. We present promising results in localizing an artificial infrasonic source and real-world experiments that suggest that we can localize free ranging elephants in the wild using this low cost infrasonic detector with an accuracy of around 10 m at distances of several hundred meters.

Dengue is one of the deadly and fast spreading diseases in Sri Lanka. The female Aedes mosquito is the dengue vector and these mosquitoes breed in clear and non-flowing water. The Public Health Inspectors (PHIs) are tasked with detecting and eliminating such water collection areas.

The human-elephant conflict is causing significant damages to the life of human and elephants in Sri Lanka. Minimizing encounters between humans and elephants is hence crucial in alleviating the human-elephant conflict. We have earlier introduced Eloc, a cost-effective localization system that is based on infrasonic emissions from wild elephants. One of the remaining challenges is a resource-efficient method to detect the elephants' infrasonic emissions. We present our efforts on devising a support vector machine (SVM) that is able to detect elephant rumbles on Eloc nodes.

Radio Tomographic Imaging (RTI) enables device free localization of physical objects by using signal attenuation in wireless networks. In this paper, we explore how existing RTI methods can be used in WiFi networks to do tomographic imaging. Moreover we analyze and evaluate the properties that affect the accuracy of WiFi tomographic imaging process.

We argue that, a wireless sensor does not have to implement a full networking stack in order to be a part of an energy efficient sensing application as long as it can deliver its sensed data to an Internet connected device over a single hop. Current hardware industry trends indicate the possibility of implementing wireless sensors for a low cost with simple capabilities. This poster highlights our preliminary work aligned with this trend and aims to open a discussion on this topic.

Real-world experiments have shown that the transmission power and the received signal strength of low-power radio transceivers used in sensornets decrease when temperature increases. We analyze how this phenomenon affects the network layer, and find that temperature fluctuations may cause undesirable behavior by sensornet routing protocols such as CTP and RPL. Furthermore, we present an approach to make these protocols robust to temperature fluctuations by augmenting the ETX link metric with temperature hints.

We present work that extends our previous Myrinet port for LAM/MPI, MPI-NP, with collective communication primitives on the NIC. This work is another step in our experiment of making the NIC MPI aware. We believe that an MPI aware control program on the NIC can deliver a richer set of performance enhancements, not just restricted to better bandwidth/latency, to MPI applications. MPI collective communication involves considerable interactions between the communication subsystems of the nodes that are not of any direct interest to the application. By migrating these talkative components to the Myrinet network interface card we allow this dialog between the nodes to happen with minimum latency. We explore the advantage of supporting several MPI collective communication routines on the NIC. These include MPI /spl I.bar/Bcast (), MPI/spl I.bar/Barrier and MPI/spl I.bar/Comm/spl I.bar/Create ().

MPI-NP II is a network processor based message manager for MPI. The objectives of MPI-NP II were to reduce host processing and to make use of the processor on the network interface board to do message scheduling and message matching. The design of MPI-NP II is based on microchannels and message rendezvous. MPI-NP II implements MPI nonblocking communication as asynchronous communication and is able to overlap computation with communication. We introduce the concept of k.safe programs to guarantee message delivery based on the available envelope resources independent of the message size. We achieve these benefits without unduly burdening the NIC processor. We obtain a latency of 22 microseconds and a bandwidth of 92 MB/s, which is comparable to other Myrinet MPI implementations that perform MPI message management on the host.