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Item Open Access Comparison of Agile scaling frameworks(Colorado State University. Libraries, 2023-08) Yeman, Robin J., author; Malaiya, Yashwant K., author; ACM, publisher21st century software development approaches, such as Agile has benefitted small initiatives with a single team building software in their ability to respond to change, reduce product delivery schedules, reduce product cost, increase product quality, and Increase employee morale. The industry has begun to question what the benefits and challenges could be if we use those same practices at scale for large Initiatives with multiple teams. There have been multiple studies on the benefits of Agile at Scale including quality, productivity, and shorter time to market. Over the last two decades dozens of Scaling frameworks have been created. However, given the large number of frameworks available, how do companies choose the right scaling framework? In this paper we review what the difference between the frameworks are by comparing their core principles. This paper began by analyzing results Digital.ai's 16th Annual state of Agile Survey which identifies the 10 most utilized frameworks according to their respondents. The paper presents a detailed analysis of the principles to assess the degree of differences among the frameworks and determine based on this which framework is the best for large scale organizations to choose.Item Open Access Partition Crossover can linearize local optima lattices of k-bounded Pseudo-Boolean functions(Colorado State University. Libraries, 2023) Whitley, Darrell, author; Ochoa, Gabriela, author; Chicanl, Francisco, author; ACM, publisherWhen Partition Crossover is used to recombine two parents which are local optima, the offspring are all local optima in the smallest hyperplane subspace that contains the two parents. The offspring can also be organized into a non-planar hypercube "lattice." Furthermore, all of the offspring can be evaluated using a simple linear equation. When a child of Partition Crossover is a local optimum in the full search space, the linear equation exactly determines its evaluation. When a child of Partition Crossover can be improved by local search, the linear equation is an upper bound on the evaluation of the associated local optimum when minimizing. This theoretical result holds for all k-bounded Pseudo-Boolean optimization problems, including MAX-kSAT, QUBO problems, as well as random and adjacent NK landscapes. These linear equations provide a stronger explanation as to why the "Big Valley" distribution of local optima exists. We fully enumerate a sample of NK landscapes to collect frequency information to complement our theoretical results. We also introduce new algorithmic contributions that can 1) expand smaller lattices in order to find larger lattices that contain additional local optima, and 2) introduce an efficient method to find new improving moves in lattices using score vectors.Item Open Access Next generation genetic algorithms: efficient Crossover and local search and new results on Crossover lattices(Colorado State University. Libraries, 2024-07) Whitley, Darrell, author; ACM, publisherItem Open Access Scheduling multi-resource satellites using genetic algorithms and permutation based representations(Colorado State University. Libraries, 2023-07) Quevedo de Carvalho, O., author; Whitley, D., author; Shetty, V., author; Jampathom, P., author; Roberts, M., author; ACM, publisherThe U.S. Navy currently deploys Genetic Algorithms to schedule multi-resource satellites. We document this real-world application and also introduce a new synthetic test problem generator. A permutation is used as the representation. A greedy scheduler then converts the permutation into a schedule which can be displayed as a Gantt chart. Surprisingly, there have been few careful comparisons of standard generational Genetic Algorithms and Steady State Genetic Algorithms for these types of problems. In addition, this paper compares different crossover operators for the multi-resource satellite scheduling problem. Finally, we look at two ways of mapping the permutation to a schedule in the form of a Gantt chart. One method gives priority to reducing conflicts, while the other gives priority to reducing overlaps of conflicting tasks. This can produce very different results, even when the evaluation function stays exactly the same.Item Open Access Evolutionary computation and tunneling at the edge of quantum computing(Colorado State University. Libraries, 2023-07) Whitley, Darrell, author; ACM, publisherItem Open Access Maximal simplification of polyhedral reductions(Colorado State University. Libraries, 2025-01-09) Narmour, Louis, author; Yuki, Tomofumi, author; Rajopadhye, Sanjay, author; ACM, publisherReductions combine collections of input values with an associative and often commutative operator to produce collections of results. When the same input value contributes to multiple outputs, there is an opportunity to reuse partial results, enabling reduction simplification. Simplification often produces a program with lower asymptotic complexity. Typical compiler optimizations yield, at best, a constant fold speedup, but a complexity improvement from, say, cubic to quadratic complexity yields unbounded speedup for sufficiently large problems. It is well known that reductions in polyhedral programs may be simplified automatically, but previous methods cannot exploit all available reuse. This paper resolves this long-standing open problem, thereby attaining minimal asymptotic complexity in the simplified program. We propose extensions to prior work on simplification to support any independent commutative reduction. At the heart of our approach is piece-wise simplification, the notion that we can split an arbitrary reduction into pieces and then independently simplify each piece. However, the difficulty of using such piece-wise transformations is that they typically involve an infinite number of choices. We give constructive proofs to deal with this and select a finite number of pieces for simplification.Item Open Access Automatic hardware pragma insertion in high-level synthesis: a non-linear programming approach(Colorado State University. Libraries, 2025-02-07) Pouget, Stéphane, author; Pouchet, Louis-Noël, author; Cong, Jason, author; ACM, publisherHigh-Level Synthesis enables the rapid prototyping of hardware accelerators, by combining a high-level description of the functional behavior of a kernel with a set of micro-architecture optimizations as inputs. Such optimizations can be described by inserting pragmas e.g., pipelining and replication of units, or even higher level transformations for HLS such as automatic data caching using the AMD/Xilinx Merlin compiler. Selecting the best combination of pragmas, even within a restricted set, remains particularly challenging and the typical state-of-practice uses design-space exploration to navigate this space. But due to the highly irregular performance distribution of pragma configurations, typical DSE approaches are either extremely time consuming, or operating on a severely restricted search space. This work proposes a framework to automatically insert HLS pragmas in regular loop-based programs, supporting pipelining, unit replication, and data caching. We develop an analytical performance and resource model as a function of the input program properties and pragmas inserted, using non-linear constraints and objectives. We prove this model provides a lower bound on the actual performance after HLS. We then encode this model as a Non-Linear Program, by making the pragma configuration unknowns of the system, which is computed optimally by solving this NLP. This approach can also be used during DSE, to quickly prune points with a (possibly partial) pragma configuration, driven by lower bounds on achievable latency. We extensively evaluate our end-to-end, fully implemented system, showing it can effectively manipulate spaces of billions of designs in seconds to minutes for the kernels evaluated.Item Open Access Segmentation and immersive visualization of brain lesions using deep learning and virtual reality(Colorado State University. Libraries, 2025-01-19) Kelley, Brendan, author; Plabst, Lucas, author; Plabst, Lena, author; ACM, publisherMagnetic resonance imaging (MRIs) are commonly used for diagnosing potential neurological disorders, however preparation and interpretation of MRI scans requires professional oversight. Additionally, MRIs are typically viewed as single cross sections of the affected regions which does not always capture the full picture of brain lesions and can be difficult to understand due to 2D's inherent abstraction of our 3D world. To address these challenges we propose a immersive visualization pipeline that combines deep learning image segmentation techniques using a VGG-16 model trained on MRI fluid attenuated inversion recovery (FLAIR) with virtual reality (VR) immersive analytics. Our visualization pipeline begins with our VGG-16 model predicting which regions of the brain are potentially affected by a disease. This output, along with the original scan, are then volumentrically rendered. These renders can then be viewed in VR using an head mounted display (HMD). Within the HMD users can move through the volumentric renderings to view the affected regions and utilize planes to view cross sections of the MRI scans. Our work provides a potential pipeline and tool for diagnosis and care.Item Open Access A unified framework for automated code transformation and pragma insertion(Colorado State University. Libraries, 2025-02-27) Pouget, Stéphane, author; Pouchet, Louis-Noël, author; Cong, Jason, author; ACM, publisherHigh-Level Synthesis compilers and Design Space Exploration tools have greatly advanced the automation of hardware design, improving development time and performance. However, achieving a good Quality of Results still requires extensive manual code transformations, pragma insertion, and tile size selection, which are typically handled separately. The design space is too large to be fully explored by this fragmented approach. It is too difficult to navigate this way, limits the exploration of potential optimizations, and complicates the design generation process. To tackle this obstacle, we propose Sisyphus, a unified framework that automates code transformation, pragma insertion, and tile size selection within a common optimization framework. By leveraging Nonlinear Programming, our approach efficiently explores the vast design space of regular loop-based kernels, automatically selecting loop transformations and pragmas that minimize latency. Evaluation against state-of-the-art frameworks, including AutoDSE, NLP-DSE, and ScaleHLS, shows that Sisyphus achieves superior Quality of Results, outperforming alternatives across multiple benchmarks. By integrating code transformation and pragma insertion into a unified model, Sisyphus significantly reduces design generation complexity and improves performance for FPGA-based systems.Item Open Access Lights, headset, tablet, action: exploring the use of hybrid user interfaces for immersive situated analytics(Colorado State University. Libraries, 2024-10-24) Zhou, Xiaoyan, author; Lee, Benjamin, author; Ortega, Francisco R., author; Batmaz, Anil Ufuk, author; Yang, Yalong, author; ACM, publisherWhile augmented reality (AR) headsets provide entirely new ways of seeing and interacting with data, traditional computing devices can play a symbiotic role when used in conjunction with AR as a hybrid user interface. A promising use case for this setup is situated analytics. AR can provide embedded views that are integrated with their physical referents, and a separate device such as a tablet can provide a familiar situated overview of the entire dataset being examined. While prior work has explored similar setups, we sought to understand how people perceive and make use of visualizations presented on both embedded visualizations (in AR) and situated visualizations (on a tablet) to achieve their own goals. To this end, we conducted an exploratory study using a scenario and task familiar to most: adjusting light levels in a smart home based on personal preference and energy usage. In a prototype that simulates AR in virtual reality, embedded visualizations are positioned next to lights distributed across an apartment, and situated visualizations are provided on a handheld tablet. We observed and interviewed 19 participants using the prototype. Participants were easily able to perform the task, though the extent the visualizations were used during the task varied, with some making decisions based on the data and others only on their own preferences. Our findings also suggest the two distinct roles that situated and embedded visualizations can have, and how this clear separation might improve user satisfaction and minimize attention-switching overheads in this hybrid user interface setup. We conclude by discussing the importance of considering the user's needs, goals, and the physical environment for designing and evaluating effective situated analytics applications.Item Open Access Integrating soft skills training into your course through a collaborative activity(Colorado State University. Libraries, 2025-02-18) Brieven, Géraldine, author; Moraes, Marcia, author; Pawelczak, Dieter, author; Vasilache, Simona, author; Donnet, Benoit, author; ACM, publisherNowadays, employers highly value soft skills, yet many students lack these fundamental abilities. Teaching soft skills involves fostering active student participation and facilitating communication of technical knowledge among peers. This approach presents challenges: (i) creating an engaging learning environment; (ii) ensuring students get timely feedback; (iii) finding an approach that is not too time-consuming for instructors to prepare. The Collaborative Design & Build (CDB) activity, described in this paper, was designed to respond to these challenges. It simulates a real-life scenario, triggering students' interest. The success of this collaborative activity hinges on students working together in a structured chain, where each team builds upon and contributes to the success of the others. This fosters student engagement and accountability as they realize the impact of their actions on the entire chain. This pedagogical approach has already been adopted by four universities abroad. This paper shows how it can be deployed in different courses. Finally, it also discusses how students perceived the activity through four soft skills: collaboration, communication, problem solving and critical thinking. These skills were selected based on their relevance, both in the context of the collaborative activity and in the job market. They are also aligned with the ''4C's of 21st Century skills''. Results show that while students initially struggled with soft skills, consistent practice throughout the semester boosted their confidence, especially in communication. This makes the activity particularly relevant in the classroom, as communication is considered as the most important soft skill for the future.Item Open Access Towards synthesis of application-specific forward error correction (FEC) codes(Colorado State University. Libraries, 2024-11-18) McClurg, Jedidiah, author; Baker, Lauren Zoe, author; Canizales, Ronaldo, author; Karki, Dilochan, author; ACM, publisherForward error correction (FEC) is a key component of modern high-bandwidth networks. Typically implemented at the physical layer, FEC attaches error-correcting codes to blocks of transmitted data, allowing some corrupted blocks to be repaired without retransmission. We outline a synthesis-based approach for automatic exploration of the FEC-code design space, focusing on Hamming codes. We formally verify the correctness of a Hamming (128, 120) code used for FEC in the recent 802.3df Ethernet standard, and provide preliminary evidence that our prototype synthesizer can leverage user-provided formal properties to generate FEC codes that are highly robust, efficiently implementable, and tuned to support specific data formats such as IEEE floating points.Item Open Access The restorative influence of virtual reality environment design(Colorado State University. Libraries, 2024-08-30) Nicoly, Jalynn Blu, author; Masters, Rachel, author; Gaddy, Vidya, author; Interrante, Victoria, author; Ortega, Francisco, author; ACM, publisherVirtual reality (VR) could support the need for easily accessible therapeutic techniques, such as viewing art and immersing oneself in nature. Our study searches for the optimal virtual environment (VE) by exploring whether beauty in moving and still VEs contributes to stress reduction and perceived restorativeness. We hypothesized that the moving forest environment would result in the most stress reduction, while the abstract art would result in the least, with additional comparisons to a still forest environment and a control condition. The control condition took place outside the virtual headset to simulate what stress reduction would look like without a nature intervention. After working with 78 participants, we found an increase in statistical significance for stress reduction and perceived restorativeness in the moving forest condition compared to the control, as measured by the Zuckerman Inventory of Personal Reactions (ZIPERS) positive affect and the Perceived Restorativeness Scale (PRS). Additionally, the PRS and heart rate measures showed greater restorativeness in the moving forest condition than in the abstract art condition. Heart rate measures also showed statistical significance between the forest image condition and the control and moving forest conditions.Item Open Access Attacks and defenses for large language models on coding tasks(Colorado State University. Libraries, 2024-10-27) Zhang, Chi, author; Wang, Zifan, author; Zhao, Ruoshi, author; Mangal, Ravi, author; Fredrikson, Matt, author; Jia, Limin, author; Pasareanu, Corina, author; ACM, publisherModern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks, including writing and reasoning about code. They improve upon previous neural network models of code, such as code2seq or seq2seq, that already demonstrated competitive results when performing tasks such as code summarization and identifying code vulnerabilities. However, these previous code models were shown vulnerable to adversarial examples, i.e., small syntactic perturbations designed to "fool" the models. In this paper, we first aim to study the transferability of adversarial examples, generated through white-box attacks on smaller code models, to LLMs. We also propose a new attack using an LLM to generate the perturbations. Further, we propose novel cost-effective techniques to defend LLMs against such adversaries via prompting, without incurring the cost of retraining. These prompt-based defenses involve modifying the prompt to include additional information, such as examples of adversarially perturbed code and explicit instructions for reversing adversarial perturbations. Our preliminary experiments show the effectiveness of the attacks and the proposed defenses on popular LLMs such as GPT-3.5 and GPT-4.Item Open Access Predicting attrition among software professionals: antecedents and consequences of burnout and engagement(Colorado State University. Libraries, 2024-12) Trinkenreich, Bianca, author; Santos, Fabio, author; Stol, Klaas-Jan, author; ACM, publisherIn this study of burnout and engagement, we address three major themes. First, we offer a review of prior studies of burnout among IT professionals and link these studies to the Job Demands-Resources (JD-R) model. Informed by the JD-R model, we identify three factors that are organizational job resources and posit that these (a) increase engagement and (b) decrease burnout. Second, we extend the JD-R by considering software professionals' intention to stay as a consequence of these two affective states, burnout and engagement. Third, we focus on the importance of factors for intention to stay, and actual retention behavior. We use a unique dataset of over 13,000 respondents at one global IT organization, enriched with employment status 90 days after the initial survey. Leveraging partial-least squares structural quation modeling and machine learning, we find that the data mostly support our theoretical model, with some variation across different subgroups of respondents. An importance-performance map analysis suggests that managers may wish to focus on interventions regarding burnout as a predictor of intention to leave. The Machine Learning model suggests that engagement and opportunities to learn are the top two most important factors that explain whether software professionals leave an organization.Item Open Access DeepSoil: a science-guided framework for generating high precision soil moisture maps by reconciling measurement profiles across in-situ and remote sensing(Colorado State University. Libraries, 2024-10-29) Khandelwal, Paahuni, author; Pallickara, Sangmi Lee, author; Pallickara, Shrideep, author; ACM, publisherSoil moisture plays a critical role in several domains and can be used to inform decision-making in agricultural settings, drought forecasting, forest fire predictions, and water conservation. Soil moisture is measured using in-situ and remote-sensing equipment. Depending on the type of equipment that is used, some challenges must be reconciled, including the density of observations, the measurement precision, and the resolutions at which these measurements are available. In particular, in-situ measurements are high-precision but sparse, while remote sensing measurements benefit from spatial coverage, albeit at lower precision and coarser resolutions. The crux of this study is to produce higher-precision soil moisture estimates at high resolutions (30m). Our methodology combines scientific models, deep networks, topographical characteristics, and information about ambient conditions alongside both in-situ and remote sensing data to accomplish this. Domain science infuses several aspects of our methodology. Our empirical benchmarks profile several aspects and demonstrate that our methodology accounts for spatial variability while accounting for both static (soil properties and elevation) and dynamically varying phenomena to generate accurate, high-precision 30m resolution soil moisture content maps.Item Open Access The impact of nature realism on the restorative quality of virtual reality forest bathing(Colorado State University. Libraries, 2024-11) Masters, Rachel, author; Nicoly, Jalynn, author; Gaddy, Vidya, author; Interrante, Victoria, author; Ortega, Francisco, author; ACM, publisherVirtual reality (VR) forest bathing for stress relief and mental health has recently become a popular research topic. As people spend more of their lives indoors and have less access to the restorative benefit of nature, having a VR nature supplement has the potential to improve quality of life. However, the optimal design of VR nature environments is an active area of investigation with many research questions to be explored. One major issue with VR is the difficulty of rendering high-fidelity assets in real time without causing cybersickness, or VR motion sickness, within the headset. Due to this limitation, we investigate if the realism of VR nature is critical for the restorative effects by comparing a low-realism nature environment to a high-realism nature environment. We only found a significant difference in the perceived restorativeness of the two environments, but after observing trends in our data toward the stress reduction potential of the high-realism environment, we suggest exploring more varieties of high and low-realism environments in future work to investigate the full potential of VR and how people respond.Item Open Access Combating spatial disorientation in a dynamic self-stabilization task using AI assistants(Colorado State University. Libraries, 2024-11-24) Mannan, Sheikh Abdul, author; Hansen, Paige, author; Vimal, Vivekanand Pandey, author; Davies, Hannah N., auhtor; DiZio, Paul, author; Krishnaswamy, Nikhil, author; ACM, publisherSpatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans.Item Open Access Fast and scalable monitoring for value-freeze operator augmented signal temporal logic(Colorado State University. Libraries, 2024-05-14) Ghorbel, Bassem, author; Prabhu, Vinayak S., author; ACM, publisherSignal Temporal Logic (STL) is a timed temporal logic formalism that has found widespread adoption for rigorous specification of properties in Cyber-Physical Systems. However, STL is unable to specify oscillatory properties commonly required in engineering design. This limitation can be overcome by the addition of additional operators, for example, signal-value freeze operators, or with first order quantification. Previous work on augmenting STL with such operators has resulted in intractable monitoring algorithms. We present the first efficient and scalable offline monitoring algorithms for STL augmented with independent freeze quantifiers. Our final optimized algorithm has a |ρ|log(|ρ|) dependence on the trace length |ρ| for most traces ρ arising in practice, and a |ρ|2 dependence in the worst case. We also provide experimental validation of our algorithms – we show the algorithms scale to traces having 100k time samples.Item Open Access A framework for profiling spatial variability in the performance of classification models(Colorado State University. Libraries, 2024-04-03) Warushavithana, Menuka, author; Barram, Kassidy, author; Carlson, Caleb, author; Mitra, Saptashwa, author; Ghosh, Sudipto, author; Breidt, Jay, author; Pallickara, Sangmi Lee, author; Pallickara, Shrideep, author; ACM, publisherScientists use models to further their understanding of phenomena and inform decision-making. A confluence of factors has contributed to an exponential increase in spatial data volumes. In this study, we describe our methodology to identify spatial variation in the performance of classification models. Our methodology allows tracking a host of performance measures across different thresholds for the larger, encapsulating spatial area under consideration. Our methodology ensures frugal utilization of resources via a novel validation budgeting scheme that preferentially allocates observations for validations. We complement these efforts with a browser-based, GPU-accelerated visualization scheme that also incorporates support for streaming to assimilate validation results as they become available.