Modern programming tools use generative AI (GenAI) to suggest code to the user as they type, interrupting their problem-solving behavior and undermining the development of their programming critical thinking skills. In this paper, we present a scaffolded programming exercise designed to support student differentiation between good and bad GenAI code suggestions based on negative expertise–that identifying why an answer is wrong is part of developing conceptual knowledge. We compare a version of the tool that showed one suggestion (correct or not), to a version that showed three suggestions (one of which was correct). We present results on performance and error rates as well as qualitative findings centered on Pintrich and DeGroot’s theory of self-regulation. Students reported that the single suggestion version better aligned with industry tools and presented a lower cognitive load. Students also reported that the multiple suggestion version caused them to slow down and think critically about the line under consideration, the overall purpose of the code, and the benefits of planning.

Programming error messages play an important role in learning to program. The cycle of program input and error message response completes a loop between the programmer and the compiler/interpreter and is a fundamental interaction between human and computer. However, error messages are notoriously problematic, especially for novices. Despite numerous guidelines citing the importance of message readability, there is little empirical research dedicated to understanding and assessing it. We report three related experiments investigating factors that influence programming error message readability. In the first two experiments we identify possible factors, and in the third we ask novice programmers to rate messages using scales derived from these factors. We find evidence that several key factors significantly affect message readability: message length, jargon use, sentence structure, and vocabulary. This provides novel empirical support for previously untested long-standing guidelines on message design, and informs future efforts to create readability metrics for programming error messages.

Most novice programmers are not explicitly aware of the problem-solving process used to approach programming problems and cannot articulate to an instructor where they are in that process. Many are now arguing that this skill, called metacognitive awareness, is crucial for novice learning. However, novices frequently learn in university CS1 courses that employ automated assessment tools (AATs), which are not typically designed to provide the cognitive scaffolding necessary for novices to develop metacognitive awareness. This paper reports on an experiment designed to understand what difficulties novice programmers currently face when learning to code with an AAT. We describe the experiences of CS1 students who participated in a think-aloud study where they were observed solving a programming problem with an AAT. Our observations show that some students mentally augmented the tool when it did not explicitly support their metacognitive awareness, while others stumbled due to the tool's lack of such support. We use these observations to formulate difficulties faced by novices that lack metacognitive awareness, compare these results to other related studies, and look toward future work in modifying AATs.

The difficulty in understanding compiler error messages can be a major impediment to novice student learning. To alleviate this issue, multiple researchers have run experiments enhancing compiler error messages in automated assessment tools for programming assignments. The conclusions reached by these published experiments appear to be conducting. We examine these experiments and propose five potential reasons for the inconsistent conclusions concerning enhanced compiler error messages: (1) students do not read them, (2) researchers are measuring the wrong thing, (3) the effects are hard to measure, (4) the messages are not properly designed, (5) the messages are properly designed, but students do not understand them in context due to increased cognitive load. We constructed mixed-methods experiments designed to address reasons 1 and 5 with a specific automated assessment tool, Athene, that previously reported inconclusive results. Testing student comprehension of the enhanced compiler error messages outside the context of an automated assessment tool demonstrated their effectiveness over standard compiler error messages. Quantitative results from a 60 minute one-on-one think-aloud study with 31 students did not show substantial increase in student learning outcomes over the control. However, qualitative results from the one-on-one think-aloud study indicated that most students are reading the enhanced compiler error messages and generally make effective changes after encountering them.

One common frustration students face when first learning to program in a compiled language is the difficulty in interpreting the compiler error messages they receive. Attempts to improve error messages have produced differing results. Two recently published papers showed conflicting results, with one showing measurable change in student behavior, and the other showing no measurable change. We conducted an experiment comparable to these two over the course of several semesters in a CS1 course. This paper presents our results in the context of previous work in this area. We improved the clarity of the compiler error messages the students receive, so that they may more readily understand their mistakes and be able to make effective corrections. Our goal was to help students better understand their syntax mistakes and, as a reasonable measure of our success, we expected to document a decrease in the number of times students made consecutive submissions with the same compilation error. By doing this, we could demonstrate that this enhancement is effective. After collecting and thoroughly analyzing our own experimental data, we found that--despite anecdotal stories, student survey responses, and instructor opinions testifying to the tool's helpfulness--enhancing compiler error messages shows no measurable benefit to students. Our results validate one of the existing studies and contradict another. We discuss some of the reasons for these results and conclude with projections for future research.

As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.

Quantifying security risk is an important and yet difficult task in enterprise network security management. While metrics exist for individual software vulnerabilities, there is currently no standard way of aggregating such metrics. We present a model that can be used to aggregate vulnerability metrics in an enterprise network, producing quantitative metrics that measure the likelihood breaches can occur within a given network configuration. A clear semantic model for this aggregation is an important first step toward a comprehensive network security metric model. We utilize existing work in attack graphs and apply probabilistic reasoning to produce an aggregation that has clear semantics and sound computation. We ensure that shared dependencies between attack paths have a proportional effect on the final calculation. We correctly reason over cycles, ensuring that privileges are evaluated without any self-referencing effect. We introduce additional modeling artifacts in our probabilistic graphical model to capture and account for hidden correlations among exploit steps. The paper shows that a clear semantic model for aggregation is critical in interpreting the results, calibrating the metric model, and explaining insights gained from empirical evaluation. Our approach has been rigorously evaluated using a number of network models, as well as data from production systems.

Enterprise network security management is a complex task of balancing security and usability, with trade-offs often necessary between the two. Past work has provided ways to identify intricate attack paths due to misconfiguration and vulnerabilities in an enterprise system, but little has been done to address how to correct the security problems within the context of various other requirements such as usability, ease of access, and cost of countermeasures. This paper presents an approach based on Boolean satisfiability solving (SAT solving) that can reason about attacks, usability requirements, cost of actions, etc. in a unified, logical framework. Preliminary results show that the approach is both effective and efficient.

Block crosstalk cancellation techniques in practical multi-user digital subscriber line (DSL) environments may involve a high computational complexity as the channel and noise statistics can vary over time. We follow an adaptive approach by designing a structurally consistent significance-test feature within the normalized least-mean-square (NLMS) adaptive crosstalk canceller, aimed to detect significant crosstalkers within a DSL binder. The proposed detection-guided NLMS adaptive partial crosstalk canceller for DSL targets the dominant crosstalkers across user lines and tones, has low run-time complexity, demonstrates significantly faster convergence, and requires smaller training sequences when compared via simulation to the equivalent standard NLMS adaptive crosstalk canceller.

In various adaptive array applications, the desired-user signal arrives from only a relatively small number of directions. The paper proposes an NLMS-based adaptive algorithm that incorporates a direction of arrival (DOA) detection criterion. The criterion stems from Akaike's information criterion and Donoho's thresholding principle. Within ‘low-dimensional’ DOA applications, the inclusion of the DOA detection criterion leads to a reduction in the number of NLMS adapted parameters. The result is significantly improved convergence and tracking speeds, as well as improved nulling of multi-user interference signals. Simulations demonstrate the favourable performance of the proposed NLMS adaptive-array system.

We present experimental results of the blind separation of independent sources from their nonlinear mixtures. The proposed EKENS (equivariant kernel nonlinear separation) algorithm is a generalization of a natural gradient algorithm and the Gram-Charlier series, which is extended in two ways: (1) to deal with nonlinear mapping; (2) to be able to adapt to the actual statistical distributions of the sources by estimating the kernel density distribution at the output signals. The observations are modelled based on nonlinear generative multilayer perceptron analysis. The theory of the EKENS learning algorithm is discussed. Simulations show that the EKENS algorithm is able to find the underlying sources from the observation, even though the data generating mapping is nonlinear and unknown.

In this paper we consider the phase degradation performance between an ideal radar path and that of a sea surface reflected signal (over a variety of sea surface heights) in an Emulated Bistatic Radar scenario. The sea surface model employed is a ID Fractal model that is based on the Generalised Weierstrass Function. Graphical and Tabular results are provided that demonstrate the absolute performance using the simulation technique discussed, for sea heights up to 0.9m.

In various estimation problems, the system being estimated may be represented by a sparse parameter vector, such that only a 'small' number of the vector elements are 'significant' or 'active'. In this paper we propose a normalised least mean square (NLMS) estimator which incorporates a least squares based active parameter criterion; such that NLMS adaptation is applied only to those system parameters detected as being active. This results in a significant improvement in convergence rates, as compared to the standard NLMS estimator. Importantly, for sparse systems, the computational cost of the newly proposed detection guided NLMS estimator is only slightly greater than that of the standard NLMS estimator.

In this paper we consider a bistatic SAR imaging system which involves a purpose built ground-based or airborne receiver and which utilizes a non-dedicated satellite (e.g. Globalstar or GPS satellites) as the transmitting signal source. We analyse the spatial resolving capability of this non-cooperative bistatic SAR imaging system. In particular, with earth observation applications in mind, we characterize quantitatively the 2-D resolving capability within the ground plane. This is demonstrated via simulations.

Aircraft that employs radar cross-section reduction techniques typically have a significantly larger bistatic radar cross-section. This paper discusses the possibility of utilising an airborne monostatic radar configuration over an oceanic region to emulate a bistatic radar. This is achieved by employing the reflective nature of the ever-present sea clutter to effectively create a "pseudo-transmitter" on the sea's surface. In addition to improving the radar cross-section of targets, this emulated bistatic radar system reduces the target's capability to locate the airborne receiver. The reflection and scattering characteristics of the sea's surface are also discussed and rudimentary models reflecting these characteristics with regard to the forward scatter region are developed.

In this paper we describe the concept of a bistatic SAR system which involves a purpose built stationary ground based receiver but which utilizes existing or proposed low earth orbit satellite (LEOS) communication systems, such as Globalstar or ICO as the transmitting signal sources. In general, such a system has a 3-D configuration (in contrast to the 2-D bistatic SAR systems examined previously in the literature). We analyse this system. With earth observation applications in mind, we characterize quantitatively the 2-D resolving capability within the ground plane. This is demonstrated via a meaningful simulation.

Signals from satellite systems like GPS, IRIDUM and Globalstar that are reflected from moving or stationary objects are utilised for their detection in a synthetic aperture bistatic radar system (SAR). The movements of the satellite and its position at different coordinates with respect to time can serve as a base for a synthetic aperture. This paper focuses on the development of a system based on the analysis of indirect signals, in particular GPS signals. The main concern in this study is the low power of the GPS signal at the earth's surface (typically about -160 dB). Due to the nature of the reflector/target, some power will be absorbed, and hence result in a further reduction in signal strength reflected by the target. However, the other equally important concern here is the ground and sea clutter, which is 18-22 dB stronger than the direct signal level. Here we present theoretical results of both the maximum detection range and land clutter contributions of the air target detection by bistatic SAR that utilizes the existing GPS satellite as the transmitting signal source.

Surveillance of large areas of the Earth's surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a need to process these images with automatic target detection (ATD) algorithms. Typically the targets being searched for are vehicles or small vessels, which occupy only a few resolution cells. Simple thresholding is usually inadequate for detection due to the high amount of noise in the images. Often the background has a discernible texture, and one form of detection is to search for anomalies in the texture caused by the presence of the target pixels. To perform this task a texture model must be able to model a variety of textures at run time, and also model these textures well enough to detect anomalies. We accomplish this with our multiscale nonparametric Markov random field (MRF) texture model.

By using a simple example we illustrate the effect of autocorrelation and cross correlation of subscriber signals on the achievable performance of adaptive echo cancelers in a four-wire telephone network.>

We quantify the relationship between the learning rate of the LMS adaptive FIR filter and its dimension when the input signal is correlated. It is argued that: Trace(R/sub n//sup -1/)/n, where n is the filter dimension and R/sub n/ is the n/spl times/n input signal covariance matrix, provides the link between convergence rate, filter dimension and input signal correlation. Analyses of this function show quantitatively that the convergence rate will deteriorate with increasing filter dimension, n, and, for sufficiently large n, with input signal correlation. For AR modelled voiced speech input signals, in particular, the convergence rate is shown to be considerably poorer than that for white signals.>