Testing Framework Research Articles

Novel multi-degree-of-freedom force–displacement mixed control framework for low loading rate substructure hybrid testing

In substructure hybrid tests, imposing realistic multi-degree-of-freedom (MDOF) displacement and force boundary conditions on a specimen involves controlling several actuators to emulate the MDOF boundary conditions of the experimental substructure. This setup presents significant challenges for decoupling control in MDOF systems. Due to the typically high stiffness of the specimen in the axial direction, force control is preferred over displacement control, which has limited accuracy in this direction. Typically, force control in the axial direction is combined with simultaneous displacement control in the softer direction, forming an MDOF force-displacement mixed control scheme. This scheme transforms force control into displacement control in the inner loop in the axial direction to ensure safe loading. The scheme involves addressing two major challenges: the nonlinear force–displacement transformation and the nonlinear coordinate transformation from the local actuator space (LAS) to the global Cartesian coordinate (GCC) system. To address these challenges, the Modified Newton–Raphson iteration integrated with proportional integral plus feed-forward (MNR-PIFF) and incremental kinematic transformation integrated with proportional integral plus feed-forward (IKT-PIFF) methods are proposed. Based on these methods, a novel MDOF force–displacement mixed control (NMFDC) framework is developed for substructure hybrid testing. A 6-DOF cyclic test simulation using the NMFDC framework was conducted to demonstrate its superiority. Furthermore, a 6-DOF cyclic test and substructure hybrid test at low loading rates were performed to validate the NMFDC framework because of the loading equipment limit. Experimental results and numerical simulations indicate that the NMFDC framework achieves swifter response with lower error levels. For applications in fast and real-time hybrid testing, dynamic loading equipment need to be used, the PIFF controller in the NMFDC framework should be replaced by adaptive time delay compensation in future developments.

Development, Acceptability, and Initial Implementation of an Interactive Text-Messaging Program for Fathers with Low Income.

This study describes the development, acceptability, and implementation of an interactive text messaging program to engage fathers enrolled in home visitation programs. We used an iterative development approach that integrated rapid testing of intervention content with acceptability feedback from program participants to examine the processes of implementation. In Study 1, we describe the rapid testing framework and present data from 171 men who provided feedback on Text4Dad content via three online surveys. In Study 2, a case study, we use administrative data from 108 fathers with whom we pilot-tested Text4Dad in three community-based home visiting programs, with the program implemented by fatherhood program community health workers (F-CHWs). Content analysis of exchanges between F-CHWs and fathers describes the specific use of Text4Dad. Across all three online surveys, fathers reported positive reviews of the Text4Dad content. The F-CHWs used Text4Dad mainly to push out information, especially that related to home visit scheduling and local events, instead of engaging in bidirectional interactions with fathers. We conclude with a set of recommendations for social service and maternal and child health providers regarding the feasibility of implementing text messaging to support home visiting in community-based settings.

Adoption of Imitation Modeling Tools in Industrial Small and Medium Enterprises: Value Creation and Competitive Advantage

Currently, value creation is considered as a major part of strategic planning and a source of competitive advantage. Competition increases and customers ask for higher service level, but resources are always limited. Small and medium enterprises (SMEs) often face the enlarged requirements of rapid changes created by the modern economic tendencies. Therefore, businesses should react promptly and without spending excessive resources (otherwise SMEs may simply stop existing). On this issue, imitation modeling tools give a considerable advantage to SMEs: almost any decisions of strategic or operational management levels can be tested before implementing real business processes. The research question arises at the point of defining the conceptual framework for testing, adopting and implementing new practices (such as digital technologies and projects) with imitation modeling tools. Currently, business excellence often depends on how and at what extent digital solutions transform the traditional way of managing operations. This study is focused on considering many factors influencing logistics business processes in SMEs. The adjusted three-zone model is suggested for achieving operational excellence in industrial SMEs with imitation modeling tools. The suggested approach is based upon the triple bottom line concept, and creates value from several perspectives: economic, social and environmental.

A generic approach for reproducible model distillation

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable “student” model to mimic the predictions made by the black box “teacher” model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough sample of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed separately for each specific class of student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the estimated fidelity of the student to the teacher. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a sample size such that the consistent student model would be selected under different pseudo samples. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process. The code is publicly available at https://github.com/yunzhe-zhou/GenericDistillation.

Functional traits and habitat use: Investigating community assembly in a montane community (Carabidae: Nebria)

AbstractThe processes that influence community assembly, such as competition for resources and environmental filtering, are often scale dependent and vary across ecotones. Trait‐based ecology provides a useful framework for testing which ecological processes most strongly influence local community composition, especially across environmental gradients where species diversity varies. Where environmental filtering dominates, species distributions are expected to be defined by strong turnover along environmental gradients, with more similar species occupying more similar habitats. Where interspecific competition dominates, species are expected to diverge in relative abundance and resource utilization at sites, so species can co‐occur. Here, we integrate measurements of functional traits, microhabitat usage, isotopic composition (δ15N and δ13C), and abundance to test the importance of environmental filtering and resource/habitat partitioning in shaping a montane ground beetle species assemblage (Carabidae: Nebriini: Nebria) in the isolated, volcanic peaks of the northern Cascades Range, USA. Across species of Nebria, body size, pronotal shape, temperature preference, and isotopic enrichment varied across habitats ([gravel, rocks 10 cm–50 cm in diameter], large rocks [>50 cm in diameter], vegetation‐covered rocks, and alpine [snowfields and talus]), and habitat/microhabitat features were reliable predictors of species presence. Resource consumption among mid‐elevation species on Mt. Rainier—the peak with the greatest species diversity—is highly overlapping. Species turnover and nestedness varied significantly across habitat gradients and peaks throughout this region and varied nearly significantly across sites. Across habitat types and sites, more similar species are more likely to coexist. These results suggest that environmental filtering is the primary process structuring this species assemblage, although we find detailed evidence for microhabitat niche partitioning among species of Nebria at the site scale.

A developmental neurotoxicity adverse outcome pathway (DNT-AOP) with voltage gate sodium channel (VGSC) inhibition as a molecular initiating event (MiE).

The adverse outcome pathway (AOP) framework serves as a practical tool for organising scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. However, a major challenge in the broader application of the AOP concept within regulatory toxicology is the development of a robust AOPs that can withstand peer review and acceptance. This is mainly due to the considerable amount of work required to substantiate the modular units of a complete AOP, which can take years from inception to completion. The methodology used here consisted of an initial assessment of a single chemical hazard using the Integrated Approach to Testing and Assessment (IATA) framework. An evidence-based approach was then used to gather empirical evidence combining systematic literature review methods with expert knowledge to ensure the effectiveness of the AOP development methodology. The structured framework used assured transparency, objectivity and comprehensiveness, and included expert knowledge elicitation for the evaluation of key event relationships (KERs). This stepwise approach led to the development of an AOP that begins with binding of chemicals to Voltage Gate Sodium Channels (VGSC/Nav) during mammalian development leading to adverse consequences in neurodevelopment evidenced as deficits in cognitive functions. Disruption of the formation of precise neural circuits by alterations in VGSC kinetics during the perinatal stages of brain development may also underlie neurodevelopmental disorders. Gaps in our understanding include the specific critical developmental windows and the quantitative relationship of binding to VGSC and subsequent disruption and cognitive function. Despite the limited quantitative information at all KER levels, regulatory applications of this AOP for DNT assessment have been identified.

Host Galaxies for Four Nearby CHIME/FRB Sources and the Local Universe FRB Host Galaxy Population

We present the host galaxies of four apparently nonrepeating fast radio bursts (FRBs), FRB 20181223C, FRB 20190418A, FRB 20191220A, and FRB 20190425A, reported in the first Canadian Hydrogen Intensity Mapping Experiment (CHIME/FRB) catalog. Our selection of these FRBs is based on a planned hypothesis testing framework where we search all CHIME/FRB Catalog-1 events that have low extragalactic dispersion measure (<100 pc cm−3), with high Galactic latitude (∣b∣ > 10°) and saved baseband data. We associate the selected FRBs with galaxies with moderate to high star formation rates located at redshifts between 0.027 and 0.071. We also search for possible multimessenger counterparts, including persistent compact radio and gravitational-wave sources, and find none. Utilizing the four FRB hosts from this study, along with the hosts of 14 published local Universe FRBs (z < 0.1) with robust host association, we conduct an FRB host demographics analysis. We find all 18 local Universe FRB hosts in our sample to be spirals (or late-type galaxies), including the host of FRB 20220509G, which was previously reported to be elliptical. Using this observation, we scrutinize proposed FRB source formation channels and argue that core-collapse supernovae are likely the dominant channel to form FRB sources. Moreover, we infer no significant difference in the host properties of repeating and apparently nonrepeating FRBs in our local Universe FRB host sample. Finally, we find the burst rates of these four apparently nonrepeating FRBs to be consistent with those of the sample of localized repeating FRBs observed by CHIME/FRB. Therefore, we encourage further monitoring of these FRBs with more sensitive radio telescopes.

Developing Flipbook-Based on Competency for Vocational High School Teacher

This research aims to improve the quality of learning in vocational high schools by strengthening teachers' teaching skills as a learning tool, encouraging innovation in teaching methods and providing a competency development model for vocational school teachers. This research uses research methods (Research Development) with the 4D Define, Design, Development, Disseminate model. This research method provides a systematic approach that includes steps and focuses on the process of developing teacher competency effectively to ensure that the flipbook developed is relevant to practical and contextual needs. The 4D model provides a planned testing and evaluation framework, so that research can provide strong empirical evidence about the success of the learning model being developed. The first stage is to determine the needs and objectives of developing the flipbook, which is then continued by designing the concept and structure of the flipbook in accordance with the curriculum and teaching needs of vocational school teachers. After that, the development team carried out the flipbook development process based on a predetermined design, by carrying out regular trials and revisions. The final step is to socialize the flipbook to vocational school teachers and distribute it to relevant stakeholders. The research results obtained in the media validation test were 86%, material 88% and language 89%. The Practicality Assessment obtained a score of 89% and classical completeness 100%. It was concluded that this medium for developing vocational teacher competency is effective as a reference choice in developing vocational teacher competency.

Autonomous driving test system under hybrid reality: The role of digital twin technology

Autonomous vehicles have attracted attention as a result of enhancements in artificial intelligence, the Internet of Things, and communication technologies. There is a priority for new testing frameworks to keep up with the increasing complexity of decision-making, connectivity, data interchange, and data transmission speed. Traditional vehicle testing tools and methods cannot meet the new testing requirements imposed by the upgrading of autonomous driving (AD) technology. They are expensive, time-intensive, and present safety hazards during testing, as well as cannot simulate hybrid real-world situations and manage real-time data efficiently. Therefore, in terms of test efficiency, cost, and safety, a smart vehicle testing in hybrid reality and evaluation method based on a digital twin (DT) is presented to speed up the development and testing of AD functions. Our model uses three-dimensional coordinate mapping, a collision detection model, and virtual scene registration to plot the AD information in the actual environment to the virtual scenario. In addition, the consistent mixed reality-based AD test model is constructed at the same time. Using this model, we demonstrated that our proposal allows for better performance of an AD test. Furthermore, the collision test demonstrates that the mixed reality system has interactive features. The performance of the system under the sampling rate of 50 ms, 200 ms, and 800 ms is compared and analyzed. Also, the experiments show that the algorithm described in this paper works better when the sampling frequency is 200 ms or more.

Integrating AI and Machine Learning in Quality Assurance for Automation Engineering

The integration of AI and Machine Learning (ML) into Quality Assurance (QA) for Automation Engineering represents a transformative shift, leveraging data-driven decision-making and automation across industries. Despite their promising benefits, the reliability, fairness, and generalizability of ML models remain significant concerns. This paper addresses these challenges by exploring the complexities inherent in assessing and validating ML programs. Firstly, it identifies obstacles such as bias, model robustness, and adaptability to new data, emphasizing the necessity for rigorous testing frameworks. Secondly, the paper reviews existing methodologies and solutions proposed in scholarly literature to enhance the assessment of ML programs, ensuring they perform as intended and meet ethical standards.This comprehensive manual serves as a guiding resource for professionals and scholars navigating the dynamic convergence of QA and ML. It underscores the need for continual learning and adaptation in an era where AI's potential is matched by the responsibilities of ethical and resilient model development. By offering profound insights and methodologies, the paper equips QA practitioners and AI enthusiasts alike to navigate the intricate terrain of quality assurance in the era of machine learning effectively.

Simulation Study on Constraining Gravitational Wave Propagation Speed by Gravitational Wave and Gamma-ray Burst Joint Observation on Binary Neutron Star Mergers

Theories of modified gravity suggest that the propagation speed of gravitational waves (GW) v g may deviate from the speed of light c. A constraint can be placed on the difference between c and v g with a simple method that uses the arrival time delay between GW and electromagnetic wave simultaneously emitted from a burst event. We simulated the joint observation of GW and short gamma-ray burst signals from binary neutron star merger events in different observation campaigns, involving advanced LIGO (aLIGO) in design sensitivity and Einstein Telescope (ET) joint-detected with Fermi/GBM. As a result, the relative precision of constraint on v g can reach ∼10−17 (aLIGO) and ∼10−18 (ET), which are one and two orders of magnitude better than that from GW170817, respectively. We continue to obtain the bound of graviton mass m g ≤ 7.1(3.2) × 10−20 eV with aLIGO (ET). Applying the Standard-Model Extension test framework, the constraint on v g allows us to study the Lorentz violation in the nondispersive, nonbirefringent limit of the gravitational sector. We obtain the constraints of the dimensionless isotropic coefficients s¯00(4) at mass dimension d = 4, which are −1×10−15<s¯00(4)<9×10−17 for aLIGO and −4×10−16<s¯00(4)<8×10−18 for ET.

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE—a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

Adaptation and Reach of a Pre-Exposure Prophylaxis Social Marketing Campaign for Latino, Latina, and Latinx Populations: Development Study.

Latino, Latina, and Latinx (Latino/a/x) individuals remain disproportionately impacted by HIV, particularly sexual minority men and transgender women. Pre-exposure prophylaxis (PrEP) is an effective means of biomedical HIV prevention, but awareness and uptake remain low among marginalized Latino/a/x populations. Social marketing campaigns have demonstrated promise in promoting PrEP in other populations but are poorly studied in Latino/a/x sexual minority men and transgender women. This study aims to (1) adapt and pilot a PrEP social marketing campaign tailored to Latino/a/x populations with a focus on sexual minority men and transgender women through community-based participatory research (CBPR) and (2) evaluate the reach and ad performance of the adapted PrEP social marketing campaign. We used the ADAPT-ITT (assessment, decision, adaptation, production, topical experts-integration, training, and testing) framework for adapting evidence-based interventions for new settings or populations. This paper presents how each phase of the ADAPT-ITT framework was applied via CBPR to create the PrEPárate ("Be PrEPared") campaign. Key community engagement strategies included shared ownership with community partners, focus groups to guide content, crowdsourcing to name the campaign, design by local Latino/a/x artists, and featuring local influencers as the faces of PrEPárate. We evaluated campaign reach and advertisement performance using social media platform metrics (paid and organic reach, impressions, unique clicks, and click-through rates [CTR]) and website use statistics from Google Analytics. The PrEPárate campaign ran in Cook County, Illinois, from April to September 2022. The campaign reached over 118,750 people on social media (55,750 on Facebook and Instagram [Meta Platforms Inc] and 63,000 on TikTok [ByteDance Ltd]). The Meta ads performed over the industry benchmark with ads featuring local transgender women (2% CTR) and cisgender sexual minority men (1.4% CTR). Of the different Grindr (Grindr Inc) ad formats piloted, the interstitial Grindr ads were the highest performing (1183/55,479, 2.13% CTR). YouTube (Google) ads were low performing at 0.11% (153/138,337) CTR and were stopped prematurely, given limits on sexual education-related content. In the first year, there were 5006 visitors to the website. Adaptation of an existing evidence-based intervention served as an effective method for developing a PrEP social marketing campaign for Latino/a/x audiences. CBPR and strong community partnerships were essential to tailor materials and provide avenues to systematically address barriers to PrEP access. Social marketing is a promising strategy to promote PrEP among underserved Latino/a/x populations.

Study on material behaviours of additively manufactured high-impact polystyrene using artificial neural networks

AbstractFused Filament Fabrication (FFF), a process parameters-dependent manufacturing method, currently dominates the additive manufacturing (AM) sector because of its prominent ability to produce parts with intricate profiles, customise products, and minimise waste. Though the effects of FFF process parameters were investigated experimentally, recent research highlighted the importance of developing numerical modelling and computational methods on optimising the FFF printing process and FFF-printed materials. This study aims to investigate the tensile strength (TS) of FFF-printed high-impact polystyrene (HIPS) via devising a systematic testing and analysis framework, which combines experimental testing, representative volume element (RVE)-finite element method (FEM), rule of mixture (ROM), and artificial neural networks (ANN). HIPS samples are fabricated using FFF considering the variations of infill density, layer thickness, nozzle temperature, raster angle, and build orientation, and tested with standard tensile testing. The rule of mixtures (ROM) and its modified version (MROM) are employed to calculate the TS of longitudinally and transversely built samples at various infill densities, respectively, while an ANN model is constructed to investigate the effect of material anisotropy precisely. The optimal ANN architecture is built with five hidden layers with the number of neurons in each layer as 44, 82, 169, 362, and 50. Although both MROM and ANN perform well on the validation set, ANN exhibits superior accuracy with only a maximum error of 0.13% for training set and 11% for validation set. The combination of the RVE-FEM, MROM, and ANN approaches can significantly improve the FFF printing process of polymers for optimisation.

Neural post-Einsteinian framework for efficient theory-agnostic tests of general relativity with gravitational waves

Neural post-Einsteinian framework for efficient theory-agnostic tests of general relativity with gravitational waves

Investigating Documented Privacy Changes in Android OS

Android has empowered third-party apps to access data and services on mobile devices since its genesis.This involves a wide spectrum of user privacy-sensitive data, such as the device ID and location. In recent years, Android has taken proactive measures to adapt its access control policies for such data, in response to the increasingly strict privacy protection regulations around the world. When each new Android version is released, its privacy changes induced by the version evolution are transparently disclosed, and we refer to them as documented privacy changes (DPCs). Implementing DPCs in Android OS is a non-trivial task, due to not only the dispersed nature of those access control points within the OS, but also the challenges posed by backward compatibility. As a result, whether the actual access control enforcement in the OS implementations aligns with the disclosed DPCs becomes a critical concern. In this work, we conduct the first systematic study on the consistency between the operational behaviors of the OS at runtime and the officially disclosed DPCs. We propose DopCheck, an automatic DPC-driven testing framework equipped with a large language model (LLM) pipeline. It features a serial of analysis to extract the ontology from the privacy change documents written in natural language, and then harnesses the few-shot capability of LLMs to construct test cases for the detection of DPC-compliance issues in OS implementations. We apply DopCheck with the latest versions (10 to 13) of Android Open Source Project (AOSP). Our evaluation involving 79 privacy-sensitive APIs demonstrates that DopCheck can effectively recognize DPCs from Android documentation and generate rigorous test cases. Our study reveals that the status quo of the DPC-compliance issues is concerning, evidenced by 19 bugs identified by DopCheck. Notably, 12 of them are discovered in Android 13 and 6 in Android 10 for the first time, posing more than 35% Android users to the risk of privacy leakage. Our findings should raise an alert to Android users and app developers on the DPC compliance issues when using or developing an app, and would also underscore the necessity for Google to comprehensively validate the actual implementation against its privacy documentation prior to the OS release.

Understanding and Detecting Annotation-Induced Faults of Static Analyzers

Static analyzers can reason about the properties and behaviors of programs and detect various issues without executing them. Hence, they should extract the necessary information to understand the analyzed program well. Annotation has been a widely used feature for different purposes in Java since the introduction of Java 5. Annotations can change program structures and convey semantics information without awareness of static analyzers, consequently leading to imprecise analysis results. This paper presents the first comprehensive study of annotation-induced faults (AIF) by analyzing 246 issues in six open-source and popular static analyzers (i.e., PMD, SpotBugs, CheckStyle, Infer, SonarQube, and Soot). We analyzed the issues' root causes, symptoms, and fix strategies and derived ten findings and some practical guidelines for detecting and repairing annotation-induced faults. Moreover, we developed an automated testing framework called AnnaTester based on three metamorphic relations originating from the findings. AnnaTester generated new tests based on the official test suites of static analyzers and unveiled 43 new faults, 20 of which have been fixed. The results confirm the value of our study and its findings.

Why did the Hornbill not cross the river? Upland habitats rather than a physical barrier limit the distribution of the Brown Hornbill

AbstractAimIn continuous environments, niche limits of species often determine their distribution limits. However, when these limits spatially coincide with a perceived dispersal barrier, the determinants of species' ranges may be confounded. We investigate the distribution pattern of the Brown Hornbill (Aves: Bucerotidae), which spans significant riverine barriers, but stops south of the Brahmaputra River. Considering its preference for low‐elevation evergreen forests, we posit that the lack of sufficient habitats north of the Brahmaputra prevents dispersal of the Brown Hornbill, and not the river itself.LocationThe Brahmaputra valley and the Indo‐Burma hotspot.TaxaAnorrhinus austeni, Aceros nipalensis.MethodsWe analysed citizen‐science occurrence data on hornbill presence against a suite of climatic, canopy‐specific and topographical predictors to model the environmental niche of the Brown Hornbill. We used presence‐only maximum entropy modelling in an information theoretic framework, in conjunction with constructing binary logistic regression models using presence and pseudoabsence data. We compared niche models of the Brown Hornbill and the Rufous‐necked hornbill, a close relative with a similar distribution, but which has spanned the Brahmaputra River.ResultsDespite the presence of suitable wet‐evergreen forests north of the Brahmaputra River, the hilly terrains in the region act as a biogeographic barrier for the Brown Hornbill, which prefers lowland evergreen forests. Further, highly suitable regions for the Brown and the Rufous‐necked Hornbills precisely delineate low and high‐elevation evergreen forests respectively, indicating that these birds are separated along an elevational axis.Main ConclusionsThe Brahmaputra River lies at the cusp of two subtly different environmental regimes. It may therefore serve as the niche limit for certain organisms and not as a physical obstacle to their dispersal. Our study implicitly predicts how widespread deforestation prevalent in the lowland evergreen forests of this region adversely impacts the distributions of organisms that depend on them, such as the Brown Hornbill. Further, our study proffers an approach to ascertain determinants of species distributions in a hypothesis testing framework.

New Virus Variant Detection Based on the Optimal Natural Metric.

The highly variable SARS-CoV-2 virus responsible for the COVID-19 pandemic frequently undergoes mutations, leading to the emergence of new variants that present novel threats to public health. The determination of these variants often relies on manual definition based on local sequence characteristics, resulting in delays in their detection relative to their actual emergence. In this study, we propose an algorithm for the automatic identification of novel variants. By leveraging the optimal natural metric for viruses based on an alignment-free perspective to measure distances between sequences, we devise a hypothesis testing framework to determine whether a given viral sequence belongs to a novel variant. Our method demonstrates high accuracy, achieving nearly 100% precision in identifying new variants of SARS-CoV-2 and HIV-1 as well as in detecting novel genera in Orthocoronavirinae. This approach holds promise for timely surveillance and management of emerging viral threats in the field of public health.

Adopting security practices in software development process: Security testing framework for sustainable smart cities

The dependence on smart city applications has expanded in recent years. Consequently, the number of cyberattack attempts to exploit smart application vulnerabilities significantly increases. Therefore, improving smart application security during the software development process is mandatory to ensure sustainable smart cities. But the challenge is how to adopt security practices in the software development process. There are Several established and mature security testing frameworks exist that consider security requirements and testing during Several already established and mature security testing frameworks exist that consider security requirements and testing during Software Development Life Cycle (SDLC), but there is a unique challenges posed by smart city applications and the need for a comprehensive approach to address the evolving threat landscape in this context. This paper proposed a framework that adopts security testing practices in all phases of the software development process. The proposed framework identifies several security activities and steps that can be applied in each phase of the software development process.