Software Systems Research Articles

Deep Configuration Performance Learning: A Systematic Survey and Taxonomy

Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled without having a thorough knowledge of the software system, but relying mainly on data, which fits precisely with the purpose of deep learning. In this paper, we conduct a comprehensive review exclusively on the topic of deep learning for performance learning of configurable software, covering 1,206 searched papers spanning six indexing services, based on which 99 primary papers were extracted and analyzed. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data, the construction of deep learning performance models, the evaluation of these models, and their utilization in various software configuration-related tasks. We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field. To promote open science, all the raw results of this survey can be accessed at our repository: https://github.com/ideas-labo/DCPL-SLR .

Repairs and Breaks Prediction for Deep Neural Networks

With the increasing prevalence of software incorporating deep neural networks (DNNs), quality assurance for these software systems has become a crucial concern. To this end, various methods have been proposed to repair the misbehavior of DNNs by modifying their weights. However, these repair methods may not meet the developer's needs for a given dataset and model. In this study, we build prediction models for repair outcomes (i.e., repairs and breaks) to help determine whether the repair method is likely to work. By using our prediction models, developers and operators of DNNs can decide whether or not to apply a repair method, and if so, which method to use. Our prediction models utilize four metrics as explanatory metrics that represent the confidence or ambiguity in the DNN predictions. We experimented with four repair methods and 10 datasets. The experimental results demonstrate that our prediction models successfully select a repair method that meets developers’ needs in 16 out of 24 cases, resulting in an average time saving of 16.29% compared to the naive method. Based on these results, our prediction models can reduce costs for developers and operators when deciding whether to employ repair methods for real-world applications of DNNs.

Cybermycelium: a reference architecture for domain-driven distributed big data systems.

The ubiquity of digital devices, the infrastructure of today, and the ever-increasing proliferation of digital products have dawned a new era, the era of big data (BD). This era began when the volume, variety, and velocity of data overwhelmed traditional systems that used to analyze and store that data. This precipitated a new class of software systems, namely, BD systems. Whereas BD systems provide a competitive advantage to businesses, many have failed to harness the power of them. It has been estimated that only 20% of companies have successfully implemented a BD project. This study aims to facilitate BD system development by introducing Cybermycelium, a domain-driven decentralized BD reference architecture (RA). The artifact was developed following the guidelines of empirically grounded RAs and evaluated through implementation in a real-world scenario using the Architecture Tradeoff Analysis Method (ATAM). The evaluation revealed that Cybermycelium successfully addressed key architectural qualities: performance (achieving <1,000 ms response times), availability (through event brokers and circuit breaking), and modifiability (enabling rapid service deployment and configuration). The prototype demonstrated effective handling of data processing, scalability challenges, and domain-specific requirements in a large-scale international company setting. The results highlight important architectural trade-offs between event backbone implementation and service mesh design. While the domain-driven distributed approach improved scalability and maintainability compared to traditional monolithic architectures, it requires significant technical expertise for implementation. This contribution advances the field by providing a validated reference architecture that addresses the challenges of adopting BD in modern enterprises.

A Survey System for Artificial Intelligence-Based Painting Using Generative Adversarial Network Techniques

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network (GAN) technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance, and safety. The results of the questionnaires are statistically analyzed. In addition, an exploratory factor analysis was conducted to extract the data of the study, which were ultimately used to calculate the weight and importance of each index using the fuzzy hierarchical analysis method. This study constructed an evaluation system for AI painting software based on GAN technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety, respectively, whose alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, 72.606%, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance, and safety can enhance the market competitiveness of the software.

The Determination of Factors for ICT Quality Assessment Model

Information and Communication Technology (ICT) is a crucial asset in business processes and high ICT quality allows for highly efficient business processes. Measuring ICT quality is identified as a crucial step towards ensuring high-quality ICT service. Over the years several ICT assessment models have been developed and each year the factors of measurement are constantly updated. This is due to constant change in technology trends and the user requirements. Thus, any existing factors within models developed previously require review and update if necessary. Therefore, the most relevant factors for ICT assessment model are investigated in this study. Through literature review a set of updated factors are developed and expert review were used to validate the factors. Factors determined in this study are: Software System and Application, Computing Hardware, Network and Communication, Lifelong Learning organizational Governance, Ecosystem Support, Security and Official Recognition. The updated and validated factors shall help future researchers in developing a model to assess ICT compliancy for measuring readiness in providing quality ICT services.

Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Software Systems

Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale software systems. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve optimal performance, which often conflicts with the frequent pattern changes observed in software systems. Moreover, the training overhead of vast MTS in large-scale software systems is unacceptably high. To address these issues, we design OmniTransfer , a model-agnostic framework that combines weighted hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large web content service provider and a network operator show that OmniTransfer significantly reduces the model initialization time by 46.49% and the training cost by 74.51%, while maintaining high accuracy in detecting anomalies.

Medical school service regions in Canada: exploring graduate retention rates across the medical education training continuum and into professional practice

ABSTRACT Purpose To create medical school service regions and examine national in-region graduate retention patterns across the medical education continuum and into professional practice as one approach to advancing social accountability in medical education. Methods Medical school service regions were created in Canada using publicly available data and mapped using Geographic Information System (GIS) software. Population size and density for each service region were calculated using census data. Retrospective data of medical graduates who completed their medical degrees between 2001–2015 (n = 19,971) were obtained from a centralized data repository and used to analyze in-region retention rates by medical specialty across the training continuum and five years into professional practice. Results Spatial inequities were observed across medical school service regions. Graduate retention patterns also varied across service region groups and medical specialties. Quebec (86.5%) and Ontario (80.4%) had above-average retention rates across the medical education continuum. Family medicine had the highest retention rates from undergraduate to postgraduate training (81.9%), while psychiatry had the highest retention rate across the training continuum and into professional practice (71.2%). The Alberta and British Columbia service region group demonstrated high retention rates across the training continuum and into professional practice and medical specialties, except for retention from undergraduate to postgraduate medical education. Conclusion This study highlights the importance of considering both medical specialty and practice location of graduates when planning and retaining the physician workforce. The observed retention patterns among graduates are a critical aspect of addressing societal needs and represent an intermediate step towards achieving health equity. Furthermore, graduate retention patterns serve as an outcome measure for schools to demonstrate their commitment to social accountability. Tracking and monitoring graduate outcomes may lead schools to actively collaborate with government agencies responsible for healthcare policy, which may ultimately improve physician workforce planning and promote more equitable healthcare access.

Twenty years later: Evaluating the Adoption of Control Flow Integrity

Memory corruption vulnerabilities still allow compromising computers through software written in a memory-unsafe language such as C/C++. This highlights that mitigation techniques to prevent such exploitations are not all widely deployed. In this paper, we introduce SeeCFI , a tool to detect the presence of a memory corruption mitigation technique called control flow integrity (CFI). We leverage SeeCFI to investigate to what extent the mitigation has been deployed in complex software systems such as Android and specific Linux distributions (Ubuntu and Debian). Our results indicate that the overall adoption of CFI (forward- and backward-edge) is increasing across Android versions ( \(\sim\) 30% in Android 13) but remains the same low ( \(\lt\) 1%) throughout different Linux versions. Our tool, SeeCFI , offers the possibility to identify which binaries in a system were compiled using the CFI option. This can be deployed by external security researchers to efficiently decide which binaries to prioritize when fixing vulnerabilities and how to fix them. Therefore, SeeCFI can help to make software systems more secure.

Exploring the Influence of Software Evolution on Mobile App Accessibility: Insights from User Reviews

Software systems constantly evolve to accommodate stakeholders' and environments' requirements that change over time. In this process, the frequent modifications can increase software complexity and negatively impact its global quality when conducted in an unstructured way. The evolutive nature of mobile environments led researchers to investigate how mobile app evolution impacts complexity, security, resource consumption, maintainability, usability, and accessibility. In particular, there has been limited research on the impact of app updates on mobile accessibility: most studies focused on tracking the number of accessibility violations found by automated tools across successive versions of a small set of applications. In a previous work, we made a contribution to this field by identifying accessibility reviews associated with app updates and prompting ChatGPT-4 to provide an overview of the main accessibility issues and enhancements perceived by users in the new releases of a mobile app. In this manuscript, we extend our previous work by adopting manual content analysis to delve deeper into our research questions and by adding new research questions associated with the identification of reviews linked to app updates, user characteristics, WCAG principles and guidelines, and user demands reported in accessibility reviews. Our results show that the accessibility barriers reported by users are mostly linked to the WCAG 2.2 Perceivable principle, and the Distinguishable and Adaptable guidelines, which includes poor color scheme, small font size, unlabeled elements, and lack of customization options. Accordingly, the consequences of the lack of accessibility is mainly connected to the difficult users experience to perceive elements of the interface (e.g. difficult to read and distinguish content, watch videos) and to use screen readers, in addition to feel discriminated against. The most common demand developers and organizations receive is to bring back some accessible feature or to fix accessibility bugs.

Comprehensive Review and Future Research Directions on ICT Standardisation

Standardisation has become imperative to retaining order and development in modern society. The simplest actions, such as train timings and the width of the railroad, would be very difficult to achieve without Standardisation. Standardisation also solves problems, such as the use of mobile devices, which requires travel abroad when out of range. We perform a large-scale quantitative analysis for papers dealing with (1) standards and (2) Information and communications technology (ICT) data in three important databases, namely Web of Science, IEEE Explore, and ACM digital library, in this paper. These three databases presented 216 articles that were divided into five categories: standard-related review and survey studies, information management across hardware and software standards, energy management standards, machine learning model classification performance, privacy-aware software system standards, and health information and communications technology standards. This paper discusses how Standardisation facilitates the planning of the entire research and innovation process by encouraging discussions regarding the specific outputs the research aims to achieve. The paper further illustrates that references related to standardisation within the call topics act as a crucial motivating factor in the decision to adopt standardisation. In conclusion, our contribution provides a better understanding of standards in peer-reviewed publications and an essential foundation for future research. In addition, we demonstrate that standards play an important role in innovation.

Quality and Shelf-Life Properties of Ready to Eat Dry-Cured Ham Slices under Different Packaging Systems during Storage.

This study has aimed to assess the quality and shelf-life stability of dry-cured ham under different packaging systems during storage. The types of packaging systems were: aerobic packing (AP), vacuum packing (VP), and modified atmosphere packaging (MAP). Pork bicep femoris muscles (n=20) were salted with 5% NaCl, 0.01% NaNO2 and 0.05% sodium erythorbate and then inoculated with Lactobacillus pentosus (4.0×109 CFU/g) and Staphylococcus carnosus (6.0×109 CFU/g). The products were cured, ripened, and dried for 12 mon by using a commercially available manufacturing process. The end products were sliced into 2 mm-thick slices, placed in pouches or trays, and packed with AP (overwrapping), VP, and MAP (70% N2 and 30% CO2). The packed samples were stored at 10°C for 84 d, and then analyzed for color, total volatile basic nitrogen (TVBN), lipid oxidation, microorganisms, tastes-related amino acids and fatty acids. The results showed that after 84 d of storage, the VP- and MAP-packed samples exhibited better color stability. Lower rates of TVBN formation and lipid oxidation were observed in VP- and MAP-packed samples (p<0.05). Noticeably, a slower decrease in sweet amino acid and unsaturated fatty acid content was found in the VP- and MAP-packed samples after 84 d of storage (p<0.05). Hence, to retain the quality, taste, and nutritional value during storage, ready-to-eat dry-cured ham slices should be packed under VP or MAP conditions.

Synergistic effect of pre-induced dislocations and ZrB2 on mechanical and thermal properties of Al/SiC composite

Al/SiC composites are considered promising materials for electronic packaging and thermal management systems, because of their high strength, low CTE, and high thermal conductivity. Despite of promising characteristics, these composites often come at the cost of ductility. To address these issues, varying content of ZrB2 was incorporated in Al/3 wt% SiC composite to fabricate high performance hybrid composites via powder metallurgy. Subsequently, the microstructure evolution, thermophysical, and mechanical properties were investigated in a systematic way. The result shows that the pre-induced dislocations were characterized in Al/3 wt% SiC/0.5 wt% ZrB2 composite, which was further quantitatively estimated by XRD peak broadening analysis. The synergistic effect of pre-induced dislocations and grain refinement brought on by ZrB2 contributed to increase hardness, elongation to fracture, and thermal conductivity, significantly. Most specifically, the hardness, ultimate tensile strength (UTS), elongation to fracture, and ultimate compressive strength (UCS) of Al/3 wt% SiC/0.5 wt% ZrB2 composite were increased to 25.9 %, 33.0 %, 245.8 %, and 32.2 %, respectively, when compared to Al/3 wt% SiC composite. In addition, thermal conductivity was increased to 226.9 W/m.K (53.7 %) and coefficient of thermal expansion (CTE) was slightly decreased, while linear thermal expansion remained consistent in the temperature range of 25–200 °C. The thermophysical properties of hybrid Al/3 wt% SiC/0.5 wt% ZrB2 composite enhanced due to the high thermal conductivity of ZrB2, grain refinement, and stabilization effect of dislocations on the thermal expansion. Therefore, it can be stated that the addition of ZrB2 have a positive influence on the hardness, strength and elongation to fracture while maintaining high TC. These promising characteristics making them appealing materials for electronic packaging and thermal management systems.

Using AI for Dynamic Resource Allocation and Performance Optimization in Software Systems

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.

Hybrid Testing System Development for Single Point Mooring Lines FOWT’s

Abstract To advance the development of various concepts for floating offshore wind turbines, it is imperative to have access to experimental data. These data are used in the validation of the tools for the simulation of floating turbines and also for the tuning of the models. CENER has developed a hybrid testing methodology that enhances the performance of wave basin tests for scaled models of Floating Offshore Wind Turbines (FOWTs). This approach allows us to apply forces and moments from the wind turbine to the floating platform without needing to replicate wind within the testing facility or create a complex model of the wind turbine rotor. However, when it comes to testing single point moored (SPM) platforms, a unique challenge arises. These platforms have an additional degree of freedom as they can freely rotate around the vertical axis of their mooring attachment (yaw degree of freedom). These type of platforms can present important misalignments with respect to the wind depending on the combination of wind, wave and current directions. Consequently, accurately capturing the impact of misalignment on platform dynamics, especially in yaw, is essential to assess the platform’s ability to self-align with the wind direction, a key factor known as weathervaning capacity. The X1Wind platform is an innovative floating wind system, based on a single point mooring solution with a downwind wind turbine configuration that allows it to be passively self-orientated. The concept has been extensively tested on prototypes at real sea conditions as well as on scaled models at wave basins. For the last wave basin testing campaign, X1Wind has entrusted CENER with the development of the hybrid testing methodology adapted to the requirements of SPM platforms. The testing campaign has covered different wave and wind conditions, including misalignment scenarios and has shown a good dynamic behavior of the platform on yaw. This study outlines the advancements made in the CENER Software-in-the-Loop (SiL) hybrid testing system for wave basin tests of SPM platforms. In order to correctly introduce the direction of the aerodynamic forces on the wind turbine under misaligned conditions, improvements on the software and hardware (loads actuator) of the SiL system were developed and tested. An actuator system consisting of 6 propellers was used. The numerical model that calculates the rotor aerodynamic loading were expanded to take into consideration the direction of the aerodynamic loads with respect to the platform. Additionally, the drag of the wind over the tower and other platform elements must be considered, as they can play a relevant role in the generation of the weathervaning moment. The study concludes that the use of the upgraded SiL hybrid testing system is an effective and afordable solution for the testing of scaled SPM platforms at wave basin facilities. It also summarizes the different considerations that the system has to overcome for the particularity of this type of testing application.

The Role of Thermal Energy Storages in Future Smart Energy Systems

This paper conducts an in-depth energy systems analysis on the role of thermal energy storages in Denmark's transition to a fully decarbonized Smart Energy System. Using the EnergyPLAN software and national-scale energy system scenarios, the research examines how the use and impact of thermal energy storages evolves during this transition. Findings indicate that thermal energy storages play an important role in minimizing fuel consumption, curtailing losses, and in improving the overall energy-efficiency and balance of supply and demand. Initially, it primarily lowers fossil fuel use, potentially by 3 TWh per year. As renewable energy increases in the system, its main focus shifts towards reducing excess electricity via power-to-heat and conserving biomass, cutting up to 1 TWh of excess electricity annually through added flexibility. Variable system costs potentially decrease by 17-67 million EUR yearly, though economic feasibility depends on the phase of the transition when investment costs are included. In a future smart- and fully decarbonized system, the economic feasibility is heavily affected by energy prices along with other heat- and storage alternatives and flexible consumption. This leads to the novel understanding that the role of thermal energy storage changes along with the transition of the energy system.

Microwave-based passive wireless electronic transformer development

Abstract With the continuous development of intelligent substations and the past demand for intelligent transformation of power grids, electronic transformers have been widely utilized. Given that the active type of electronic transformer was susceptible to electromagnetic interference, particularly on the high-voltage power supply side, disturbances were challenging to evade, leading to its poor reliability. Based on microwave wireless transmission technology, this study theoretically proposed a dynamic wireless energy supply link for electronic transformers and designed a passive wireless electronic transformer utilizing microwave technology. Simultaneously, it developed the corresponding software and hardware systems, constructed an experimental test platform, conducted tests in high-voltage environments, and verified its performance by comparing it with active wired methods. The results demonstrated that the microwave-based passive wireless electronic transformer developed in this paper had good reliability and could be widely applied in practice.

EMPOWERING INDEPENDENT LEARNING THROUGH ASSISTIVE TECHNOLOGY: A QUANTITATIVE STUDY ON ITS IMPACT FOR STUDENTS WITH DISABILITIES

BACKGROUND The integration of assistive technologies in educational settings offers a promising avenue to enhance the learning experience for students with disabilities. These technologies, including voice recognition software, screen readers, and customizable learning management systems (LMS), have the potential to promote autonomy and engagement by accommodating diverse learning needs. This research will evaluate the effectiveness of these assistive technologies in supporting independent learning among these students. RESEARCH AIM The research aims to assess the usage and accessibility of assistive technologies in GBS, quantify their impact on students with disabilities, and identify the most effective technologies for enhancing learning outcomes. METHODS The study will involve surveys and performance metrics analysis among students with disabilities across various campuses of GBS. Statistical methods, including regression analysis and t-tests, will be employed to analyse the data. RESULTS Expected findings include detailed insights into assistive technology usage patterns and quantitative evidence of their impact on independent learning and academic performance. Recommendations for effective assistive technologies will be provided. CONCLUSION This research is expected to offer empirical evidence on the role of assistive technologies in supporting independent learning for students with disabilities, guiding educational institutions towards creating more inclusive environments.

The extent of outdoor food and beverage advertising surrounding primary and secondary schools in poorly resourced townships in Cape Town, South Africa

BackgroundThe targeted marketing of unhealthy food and beverages to vulnerable populations, particularly children and adolescents in lower-income communities, is pervasive and coincides with increased non-communicable disease (NCD) rates. This study examined the extent of food and beverage advertising surrounding schools in three townships in Cape Town, South Africa: Gugulethu, Nyanga, and Kensington. MethodologyTrained fieldworkers used web-based surveys on smartphones to capture all observable outdoor food and beverage advertisements in the three townships. Surveys recorded product brand, type, placement, Global Positioning System (GPS) coordinates, and a digital photograph. Advertisements were categorised as sugar-sweetened beverages (SSBs), unhealthy food (excluding beverages), alcohol, staple food, and miscellaneous food, following the INFORMAS Outdoor Food Advertising Protocol. Advertisements visible within 100m- and 400m buffer zones surrounding primary and secondary schools were mapped using the Quantum Geographic Information System (QGIS) software. Descriptive analyses were conducted to determine the extent of outdoor food and beverage advertising surrounding schools. ResultsWe mapped 26 primary and 7 secondary schools, observing 253 and 1587 food and beverage advertisements within 100m and 400m of schools, respectively. Unhealthy advertisements comprised 69.9% and 68.5% of all observations within 100m- and 400m zones, respectively. There were 117 SSB, 57 unhealthy food, 3 alcohol, 64 staple food, and 12 miscellaneous food advertisements within 100m zones. Nyanga zones contained the most SSB (71, 60.7%) and unhealthy food advertisements (37, 64.9%). Secondary schools had higher SSB advertisement rates than primary schools (p < 0.001). Within 400m zones, there were 623 SSB, 370 unhealthy food, 93 alcohol, 445 staple food, and 56 miscellaneous food advertisements, with Nyanga leading in SSB (335, 53.8%) and unhealthy food advertisements (224, 54.9%). ConclusionThe results highlight a concerning level of exposure to unhealthy food and beverage advertisements around schools, particularly secondary schools in low-income townships. This could have implications for the dietary choices and health of students in these areas. The findings underscore the need for policy interventions and community awareness programs to address and regulate the advertising environment around schools, promoting healthier food and beverage choices for students.

Ensuring the Correctness and Reliability of CBPS System Using Event‐B

ABSTRACTDuring the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

Desarrollo de un sistema y de un proceso como un medio para lograr la repetibilidad de resultados en la fermentación de café de baja altura

This article takes as a case study coffee grown by micro and small producers at altitudes below 1000 m a.s.l., in this scenario there is a problem during the fermentation stage. Artisanal fermentation is uncertain and does not achieve repeatability of results, therefore, the quality of the coffee cannot be guaranteed. The objective of the present work is to contribute to the solution of this problem by using technology and the implementation of processes that lead to the consistency of the results during fermentation. The technology will be used to register and control fermentation parameters such as temperature, pressure, among others. The processes will control activities such as cherry selection, product and components handling during the fermentation stage. The proposed solution will be implemented in the XaTiwan organization, a small coffee producer in the Sierra Norte of the State of Puebla. This article includes the early phases of the development cycle of a cloud-based software system to automate the fermentation process with which it seeks to achieve repeatability of results at this stage. This will contribute to control the quality of coffee grown in low altitude areas.