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The Cancer Registry of Norway (CRN) collects and processes cancer-related data for patients in Norway. For this, it employs a sociotechnical software system that evolves with changing requirements and medical standards. The current practice is to manually test CRN's system to prevent faults and ensure its dependability. This paper focuses on automatically testing GURI, the CRN's medical rule engine, using a system-level testing tool, EvoMaster, in both its black-box and white-box modes, and a novel CRN-specific EvoMaster-based tool, EvoGURI. We empirically evaluate the tools' effectiveness regarding code coverage, errors found, domain-specific rule coverage, and ability to identify artificial faults ten versions of GURI. Our results show that all the tools achieve similar code coverage and identified a similar number of errors. For rule coverage, EvoGURI and EvoMaster's black-box mode produce test suites that cover the highest number of rules with Pass, Fail, and Warning results. The test suites of EvoGURI and two EvoMaster white-box tools identify the most faults in a mutation testing experiment. Based on our findings, we recommend using EvoGURI in CRN's current practice. Finally, we present key takeaways and outline open research questions for the research community., Comment: 17 pages, 2 figures, 8 tables; journal extension of arXiv:2308.12805

The Cancer Registry of Norway (CRN) is a part of the Norwegian Institute of Public Health (NIPH) and is tasked with producing statistics on cancer among the Norwegian population. For this task, CRN develops, tests, and evolves a software system called Cancer Registration Support System (CaReSS). It is a complex socio-technical software system that interacts with many entities (e.g., hospitals, medical laboratories, and other patient registries) to achieve its task. For cost-effective testing of CaReSS, CRN has employed EvoMaster, an AI-based REST API testing tool combined with an integrated classical machine learning model. Within this context, we propose Qlinical to investigate the feasibility of using, inside EvoMaster, a Quantum Neural Network (QNN) classifier, i.e., a quantum machine learning model, instead of the existing classical machine learning model. Results indicate that Qlinical can achieve performance comparable to that of EvoClass. We further explore the effects of various QNN configurations on performance and offer recommendations for optimal QNN settings for future QNN developers.

Quantum software is a key enabler for the revolutionary applications promised by Quantum Computing (QC), a field poised to transform industries and attract significant investment. However, quantum software engineering (QSE) faces unique ethical challenges that must be addressed early to ensure responsible and equitable development. This vision paper explores the role of ethics and specifically diversity and inclusion (D&I) considerations in QSE, emphasising the importance of embedding these principles throughout the quantum software lifecycle. Neglecting D&I could lead to power imbalances, algorithmic biases, and limited innovation, ultimately hindering QC's potential to benefit society equitably. By drawing lessons from classical software and artificial intelligence, we discuss our vision for integrating D&I into QSE, for a more transparent, fair, and accountable future for quantum technologies. This paper aims to initiate discussion within the research community and lay the foundation for the ethical development of quantum software that promotes social equity.

Machine learning has been extensively applied for various classical software testing activities such as test generation, minimization, and prioritization. Along the same lines, recently, there has been interest in applying quantum machine learning to software testing. For example, Quantum Extreme Learning Machines (QELMs) were recently applied for testing classical software of industrial elevators. However, most studies on QELMs, whether in software testing or other areas, used ideal quantum simulators that fail to account for the noise in current quantum computers. While ideal simulations offer insight into QELM's theoretical capabilities, they do not enable studying their performance on current noisy quantum computers. To this end, we study how quantum noise affects QELM in three industrial and real-world classical software testing case studies, providing insights into QELMs' robustness to noise. Such insights assess QELMs potential as a viable solution for industrial software testing problems in today's noisy quantum computing. Our results show that QELMs are significantly affected by quantum noise, with a performance drop of 250% in regression tasks and 50% in classification tasks. Although introducing noise during both ML training and testing phases can improve results, the reduction is insufficient for practical applications. While error mitigation techniques can enhance noise resilience, achieving an average 3.0% performance drop in classification, but their effectiveness varies by context, highlighting the need for QELM-tailored error mitigation strategies.

Healthcare Internet of Things (IoT) applications often integrate various third-party healthcare applications and medical devices through REST APIs, resulting in complex and interdependent networks of REST APIs. Oslo City's healthcare department collaborates with various industry partners to develop such healthcare IoT applications enriched with a diverse set of REST APIs. Following the DevOps process, these REST APIs continuously evolve to accommodate evolving needs such as new features, services, and devices. Oslo City's primary goal is to utilize automated solutions for continuous testing of these REST APIs at each evolution stage, thereby ensuring their dependability. Although the literature offers various automated REST API testing tools, their effectiveness in regression testing of the evolving REST APIs of healthcare IoT applications within a DevOps context remains undetermined. This paper evaluates state-of-the-art and well-established REST API testing tools-specifically, RESTest, EvoMaster, Schemathesis, RESTler, and RestTestGen-for the regression testing of a real-world healthcare IoT application, considering failures, faults, coverage, regressions, and cost. We conducted experiments using all accessible REST APIs (17 APIs with 120 endpoints), and 14 releases evolved during DevOps. Overall, all tools generated tests leading to several failures, 18 potential faults, up to 84% coverage, 23 regressions, and over 80% cost overhead.

Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices of various types. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants, and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City's health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in OsloCity's context using five widely-used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT's ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.

Smart medical devices are an integral component of the healthcare Internet of Things (IoT), providing patients with various healthcare services through an IoT-based application. Ensuring the dependability of such applications through system and integration-level testing mandates the physical integration of numerous medical devices, which is costly and impractical. In this context, digital twins of medical devices play an essential role in facilitating testing automation. Testing with digital twins without accounting for uncertain environmental factors of medical devices leaves many functionalities of IoT-based healthcare applications untested. In addition, digital twins operating without environmental factors remain out of sync and uncalibrated with their corresponding devices functioning in the real environment. To deal with these challenges, in this paper, we propose a model-based approach (EnvDT) for modeling and simulating the environment of medical devices' digital twins under uncertainties. We empirically evaluate the EnvDT using three medicine dispensers, Karie, Medido, and Pilly connected to a real-world IoT-based healthcare application. Our evaluation targets analyzing the coverage of environment models and the diversity of uncertain scenarios generated for digital twins. Results show that EnvDT achieves approximately 61% coverage of environment models and generates diverse uncertain scenarios (with a near-maximum diversity value of 0.62) during multiple environmental simulations.

Human immune system contains white blood cells (WBC) that are good indicator of many diseases like bacterial infections, AIDS, cancer, spleen, etc. White blood cells have been sub classified into four types: monocytes, lymphocytes, eosinophils and neutrophils on the basis of their nucleus, shape and cytoplasm. Traditionally in laboratories, pathologists and hematologists analyze these blood cells through microscope and then classify them manually. This manual process takes more time and increases the chance of human error. Hence, there is a need to automate this process. In this paper, first we have used different CNN pre-train models such as ResNet-50, InceptionV3, VGG16 and MobileNetV2 to automatically classify the white blood cells. These pre-train models are applied on Kaggle dataset of microscopic images. Although we achieved reasonable accuracy ranging between 92 to 95%, still there is need to enhance the performance. Hence, inspired by these architectures, a framework has been proposed to automatically categorize the four kinds of white blood cells with increased accuracy. The aim is to develop a convolution neural network (CNN) based classification system with decent generalization ability. The proposed CNN model has been tested on white blood cells images from Kaggle and LISC datasets. Accuracy achieved is 99.57% and 98.67% for both datasets respectively. Our proposed convolutional neural network-based model provides competitive performance as compared to previous results reported in literature.

Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used 300 gastroenterology board exam-style multiple-choice questions, 138 of which contain images to systematically assess the impact of model configurations and parameters and prompt engineering strategies utilizing GPT-3.5. Next, we assessed the performance of proprietary and open-source LLMs (versions), including GPT (3.5, 4, 4o, 4omini), Claude (3, 3.5), Gemini (1.0), Mistral, Llama (2, 3, 3.1), Mixtral, and Phi (3), across different interfaces (web and API), computing environments (cloud and local), and model precisions (with and without quantization). Finally, we assessed accuracy using a semiautomated pipeline. Results: Among the proprietary models, GPT-4o (73.7%) and Claude3.5-Sonnet (74.0%) achieved the highest accuracy, outperforming the top open-source models: Llama3.1-405b (64%), Llama3.1-70b (58.3%), and Mixtral-8x7b (54.3%). Among the quantized open-source models, the 6-bit quantized Phi3-14b (48.7%) performed best. The scores of the quantized models were comparable to those of the full-precision models Llama2-7b, Llama2--13b, and Gemma2-9b. Notably, VLM performance on image-containing questions did not improve when the images were provided and worsened when LLM-generated captions were provided. In contrast, a 10% increase in accuracy was observed when images were accompanied by human-crafted image descriptions. Conclusion: In conclusion, while LLMs exhibit robust zero-shot performance in medical reasoning, the integration of visual data remains a challenge for VLMs. Effective deployment involves carefully determining optimal model configurations, encouraging users to consider either the high performance of proprietary models or the flexible adaptability of open-source models., Comment: Manuscript Pages: 34, Figures: 7, Tables: 2, Supplementary File Pages: 35, Data Transparency Statement: Code is available at: https://github.com/Sdamirsa/LLM-VLM-in-Gastroenterology . Study data from American College of Gastroenterology (ACG) are restricted and available upon request with ACG permission. Correction: updated abstract considering Llama3.1 results

Refurbishing laptops extends their lives while contributing to reducing electronic waste, which promotes building a sustainable future. To this end, the Danish Technological Institute (DTI) focuses on the research and development of several applications, including laptop refurbishing. This has several steps, including cleaning, which involves identifying and removing stickers from laptop surfaces. DTI trained six sticker detection models (SDMs) based on open-source object detection models to identify such stickers precisely so these stickers can be removed automatically. However, given the diversity in types of stickers (e.g., shapes, colors, locations), identification of the stickers is highly uncertain, thereby requiring explicit quantification of uncertainty associated with the identified stickers. Such uncertainty quantification can help reduce risks in removing stickers, which, for example, could otherwise result in damaging laptop surfaces. For uncertainty quantification, we adopted the Monte Carlo Dropout method to evaluate the six SDMs from DTI using three datasets: the original image dataset from DTI and two datasets generated with vision language models, i.e., DALL-E-3 and Stable Diffusion-3. In addition, we presented novel robustness metrics concerning detection accuracy and uncertainty to assess the robustness of the SDMs based on adversarial datasets generated from the three datasets using a dense adversary method. Our evaluation results show that different SDMs perform differently regarding different metrics. Based on the results, we provide SDM selection guidelines and lessons learned from various perspectives., Comment: 18 pages, 6 figures, 4 tables

The most promising applications of quantum computing are centered around solving search and optimization tasks, particularly in fields such as physics simulations, quantum chemistry, and finance. However, the current quantum software testing methods face practical limitations when applied in industrial contexts: (i) they do not apply to quantum programs most relevant to the industry, (ii) they require a full program specification, which is usually not available for these programs, and (iii) they are incompatible with error mitigation methods currently adopted by main industry actors like IBM. To address these challenges, we present QOPS, a novel quantum software testing approach. QOPS introduces a new definition of test cases based on Pauli strings to improve compatibility with different quantum programs. QOPS also introduces a new test oracle that can be directly integrated with industrial APIs such as IBM's Estimator API and can utilize error mitigation methods for testing on real noisy quantum computers. We also leverage the commuting property of Pauli strings to relax the requirement of having complete program specifications, making QOPS practical for testing complex quantum programs in industrial settings. We empirically evaluate QOPS on 194,982 real quantum programs, demonstrating effective performance in test assessment compared to the state-of-the-art with a perfect F1-score, precision, and recall. Furthermore, we validate the industrial applicability of QOPS by assessing its performance on IBM's three real quantum computers, incorporating both industrial and open-source error mitigation methods.

Foundation models are trained on a large amount of data to learn generic patterns. Consequently, these models can be used and fine-tuned for various purposes. Naturally, studying such models' use in the context of digital twins for cyber-physical systems (CPSs) is a relevant area of investigation. To this end, we provide perspectives on various aspects within the context of developing digital twins for CPSs, where foundation models can be used to increase the efficiency of creating digital twins, improve the effectiveness of the capabilities they provide, and used as specialized fine-tuned foundation models acting as digital twins themselves. We also discuss challenges in using foundation models in a more generic context. We use the case of an autonomous driving system as a representative CPS to give examples. Finally, we provide discussions and open research directions that we believe are valuable for the digital twin community.

Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. Large Visual Language models (VLMs) have been found effective for multiple downstream medical tasks that rely on both imaging and text data. However, lesion level detection and subsequent diagnosis using VLMs have not been explored yet. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. CADe trains on a prompt suite on input computed tomography (CT) scans by using the CLIP text encoder through prefix tuning. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning. Training and validation of CADe and CADx have been done using one of the largest publicly available datasets, called LIDC. To check the generalization ability of the model, it is also evaluated on a challenging dataset, LUNGx. Our experimental results show that the proposed methods achieve a sensitivity of 0.86 compared to 0.76 that of other fully supervised methods.The source code, datasets and pre-processed data can be accessed using the link

Quantum computing promises polynomial and exponential speedups in many domains, such as unstructured search and prime number factoring. However, quantum programs yield probabilistic outputs from exponentially growing distributions and are vulnerable to quantum-specific faults. Existing quantum software testing (QST) approaches treat quantum superpositions as classical distributions. This leads to two major limitations when applied to quantum programs: (1) an exponentially growing sample space distribution and (2) failing to detect quantum-specific faults such as phase flips. To overcome these limitations, we introduce a QST approach, which applies a reduction algorithm to a quantum program specification. The reduced specification alleviates the limitations (1) by enabling faster sampling through quantum parallelism and (2) by performing projective measurements in the mixed Hadamard basis. Our evaluation of 143 quantum programs across four categories demonstrates significant improvements in test runtimes and fault detection with our reduction approach. Average test runtimes improved from 169.9s to 11.8s, with notable enhancements in programs with large circuit depths (383.1s to 33.4s) and large program specifications (464.8s to 7.7s). Furthermore, our approach increases mutation scores from 54.5% to 74.7%, effectively detecting phase flip faults that non-reduced specifications miss. These results underline our approach's importance to improve QST efficiency and effectiveness.

In wireless networks assisted by intelligent reflecting surfaces (IRSs), jointly modeling the signal received over the direct and indirect (reflected) paths is a difficult problem. In this work, we show that the network geometry (locations of serving base station, IRS, and user) can be captured using the so-called triangle parameter $\Delta$. We introduce a decomposition of the effect of the combined link into a signal amplification factor and an effective channel power coefficient $G$. The amplification factor is monotonically increasing with both the number of IRS elements $N$ and $\Delta$. For $G$, since an exact characterization of the distribution seems unfeasible, we propose three approximations depending on the value of the product $N\Delta$ for Nakagami fading and the special case of Rayleigh fading. For two relevant models of IRS placement, we prove that their performance is identical if $\Delta$ is the same given an $N$. We also show that no gains are achieved from IRS deployment if $N$ and $\Delta$ are both small. We further compute bounds on the diversity gain to quantify the channel hardening effect of IRSs. Hence only with a judicious selection of IRS placement and other network parameters, non-trivial gains can be obtained.

To reuse the scarce spectrum efficiently, a large full-duplex cellular network with integrated sensing and communication (ISAC) is studied. Monostatic detection at the base station (BS) is considered. At the BS, we receive two signals: the communication-mode uplink signal to be decoded and the radar-mode signal to be detected. After self-interference cancellation (SIC), inspired by NOMA, successive interference cancellation (SuIC) is a natural strategy at the BS to retrieve both signals. However, the ordering of SuIC, usually based on some measure of channel strength, is not clear as the radar-mode target is unknown. The detection signal suffers a double path-loss making it vulnerable, but the uplink signal to be decoded originates at a user which has much lower power than the BS making it weak as well. Further, the intercell interference from a large network reduces the channel disparity between the two signals. We investigate the impact of both SuIC orders at the BS, i.e., decoding $1^{st}$ or detecting $1^{st}$ and highlight the importance of careful order selection. We find the existence of a threshold target distance before which detecting $1^{st}$ is superior and decoding $2^{nd}$ does not suffer much. After this distance, both decoding $1^{st}$ and detecting $2^{nd}$ is superior. Similarly, a threshold UE power exists after which the optimum SuIC order changes. We consider imperfections in SIC; this helps highlight the vulnerability of the decoding and detection in the setup.

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective -key qualities of any industry-grade software-mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering., Comment: Extended version of the previous FSE paper for the ACM TOSEM Special Issue

Stateflow models are widely used in the industry to model the high-level control logic of Cyber-Physical Systems (CPSs) in Simulink--the defacto CPS simulator. Many approaches exist to test Simulink models, but once a fault is detected, the process to repair it remains manual. Such a manual process increases the software development cost, making it paramount to develop novel techniques that reduce this cost. Automated Program Repair (APR) techniques can significantly reduce the time for fixing bugs by automatically generating patches. However, current approaches face scalability issues to be applicable in the CPS context. To deal with this problem, we propose an automated search-based approach called FlowRepair, explicitly designed to repair Stateflow models. The novelty of FlowRepair includes, (1) a new algorithm that combines global and local search for patch generation; (2) a definition of novel repair objectives (e.g., the time a fault remained active) specifically designed for repairing CPSs; and (3) a set of mutation operators to repair Stateflow models automatically. We evaluated FlowRepair with three different case study systems and a total of nine faulty stateflow models. Our experiments suggest that (1) Flo wRepaircan fix bugs in stateflow models, including models with multiple faults; (2) FlowRepair surpasses or performs similarly to a baseline APR technique inspired by a well-known CPS program repair approach. Besides, we provide both a replication package and a live repository, paving the way towards the APR of CPSs modeled in Simulink.

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as text generation, summarization, and classification. Given that such models are trained on a humongous amount of online knowledge, we hypothesize that LLMs can assess whether driving scenarios generated by autonomous driving testing techniques are realistic, i.e., being aligned with real-world driving conditions. To test this hypothesis, we conducted an empirical evaluation to assess whether LLMs are effective and robust in performing the task. This reality check is an important step towards devising LLM-based autonomous driving testing techniques. For our empirical evaluation, we selected 64 realistic scenarios from \deepscenario--an open driving scenario dataset. Next, by introducing minor changes to them, we created 512 additional realistic scenarios, to form an overall dataset of 576 scenarios. With this dataset, we evaluated three LLMs (\gpt, \llama, and \mistral) to assess their robustness in assessing the realism of driving scenarios. Our results show that: (1) Overall, \gpt achieved the highest robustness compared to \llama and \mistral, consistently throughout almost all scenarios, roads, and weather conditions; (2) \mistral performed the worst consistently; (3) \llama achieved good results under certain conditions; and (4) roads and weather conditions do influence the robustness of the LLMs.

Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes quantum dynamics and an easy-training strategy to solve problems such as classification and regression efficiently. Although QELM has many potential benefits, its real-world applications remain limited. To this end, we present QELM's industrial application in the context of elevators, by proposing an approach called QUELL. In QUELL, we use QELM for the waiting time prediction related to the scheduling software of elevators, with applications for software regression testing, elevator digital twins, and real-time performance prediction. The scheduling software has been implemented by our industrial partner Orona, a globally recognized leader in elevator technology. We demonstrate that QUELL can efficiently predict waiting times, with prediction quality significantly better than that of classical ML models employed in a state-of-the-practice approach. Moreover, we show that the prediction quality of QUELL does not degrade when using fewer features. Based on our industrial application, we further provide insights into using QELM in other applications in Orona, and discuss how QELM could be applied to other industrial applications.

The Cancer Registry of Norway (CRN) uses an automated cancer registration support system (CaReSS) to support core cancer registry activities, i.e, data capture, data curation, and producing data products and statistics for various stakeholders. GURI is a core component of CaReSS, which is responsible for validating incoming data with medical rules. Such medical rules are manually implemented by medical experts based on medical standards, regulations, and research. Since large language models (LLMs) have been trained on a large amount of public information, including these documents, they can be employed to generate tests for GURI. Thus, we propose an LLM-based test generation and differential testing approach (LLMeDiff) to test GURI. We experimented with four different LLMs, two medical rule engine implementations, and 58 real medical rules to investigate the hallucination, success, time efficiency, and robustness of the LLMs to generate tests, and these tests' ability to find potential issues in GURI. Our results showed that GPT-3.5 hallucinates the least, is the most successful, and is generally the most robust; however, it has the worst time efficiency. Our differential testing revealed 22 medical rules where implementation inconsistencies were discovered (e.g., regarding handling rule versions). Finally, we provide insights for practitioners and researchers based on the results., Comment: 12 pages, 6 figures, 4 tables, 1 listing

We propose a new approach to search for light dark matter (DM), with keV-GeV mass, via inelastic nucleus scattering at large-volume neutrino detectors such as Borexino, DUNE, Super-K, Hyper-K, and JUNO. The approach uses inelastic nuclear scattering of cosmic-ray boosted DM, enabling a low background search for DM in these experiments. Large neutrino detectors, with higher thresholds than dark matter detectors, can be used, since the nuclear deexcitation lines are O(10) MeV. Using a hadrophilic dark-gauge-boson-portal model as a benchmark, we show that the nuclear inelastic channels generally provide better sensitivity than the elastic scattering for a large region of light DM parameter space., Comment: 8 pages, 3 figures

In this work, we provide a system level analysis of integrated sensing and communication (ISAC) systems, where a setup with a mono-static dual-functional radar communication base station is assumed. We derive the ISAC signal-to-noise ratio (SNR) equation that relates communication and radar SNR for different distances. We also derive the ISAC range equation, which can be used for sensing-assisted beamforming applications. Specifically, we show that increasing the frequency and bandwidth is more favorable to the radar application in terms of relative SNR and range while increasing the transmit power is more favorable to communications. Numerical examples reveal that if the range for communication and radar is desired to be in the same order, the ISAC system should operate in mmWave or sub-THz bands, whereas sub-6GHz allows scenarios where the communication range is of orders of magnitude higher than that of radar.