An improved analytic hierarchy process model on Software Quality Evaluation

In Indonesia, education funding provided by the central government is made up of the components of personnel costs, investment costs, and operating costs. Since its implementation in 2005, the program has found several obstacles. It’s crucial to have an optimization tool to overcome the complications. The main problems that have arisen in the allocation of these funds so far have been lacking in planning and the lack of participation of the school ecosystem, in particular in determining the planning, planning documents and the priority scale of the projects. Program and activity plans based on the School Operating Assistance (BOS) platform. The Analytical Hierarchy Process (AHP) model is a prioritization approach based on determining the priority scale on the comparison of one option with another. The AHP model is a powerful but simple method that is usually used to make decisions. This approach has been implemented in various sectors, business, government, management, and education. In this study, the AHP model was used to determine the priority scale on the criteria of BOS allocated funds in the current year so that it could be absorbed optimally and can be utilized properly in its implementation for Vocational School activities. The results of the study show that the use of AHP makes it easier for school residents to prioritize the use of funds for school activities. Using the AHP model allows managers to record strategic goals using weighted criteria as the basis for prioritizing the use of funds. AHP can also work among different stakeholders in the school (principals, treasurers, teachers, and committee, parents/guardians) to participate in decisions about the use of BOS funds, to improve the quality and form of decision support taken to improve school performance.

Software stakeholders, including developers, managers, and end users, require high quality software products. Several works have aimed to identify software quality, but the quality of software products is often not comprehensively, specifically, or effectively defined because previous approaches have focused on certain quality aspects. Moreover, the evaluation results of quality metrics often depend on software stakeholders so that it is often hard to compare quality evaluation results across software products. ISO/IEC has tried to define evaluation methods for the quality of software products and provide common standards, called the SQuaRE (Systems and software Quality Requirements and Evaluation) series including ISO/IEC 25022:2016 and ISO/IEC 25023:2016. However, the SQuaRE series include ambiguous metrics so that it is not always easy to apply the series to products and compare results. In this paper, we propose a SQuaRE-based software quality evaluation framework, which successfully concretized many product metrics and quality in use metrics originally defined in the SQuaRE series1. Through a case study targeting a commercial software product, we confirmed that our framework is concretely applicable to the software package/service product.

У статті розкриваються погляди на методологію та порядок оцінювання спеціального програмного забезпечення з урахуванням вимог Національних стандартів України ДСТУ ISO/ІЕС 25010:2016 (ISO/ІЕС 25010:2011, IDT). “Інженерія систем і програмних засобів. Вимоги до якості систем і програмних засобів та її оцінювання. Моделі якості системи та програмних засобівˮ та ДСТУ ІS0/ІЕС 9126-1:2013 (ІS0/ІЕС 9126 1:2001, IDТ). “Програмна інженерія. Якість продукту. Частина 1. Модель якостіˮ. Як приклад розглядається перевірка якості спеціального програмного забезпечення для управління військами.

The analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.

System and software quality evaluation is an important method of quality assurance, and its standardization provides quality requirements and evaluation supported by quality measurement. ISO/IEC organizations have also published a series of standards on system and software quality requirements and evaluation, which is significant for assuring the quality of system and software. This paper introduces quality evaluation based on SQuaRE series standards, and analyzes the relationships among quality model, measurement, requirements, evaluation. Besides, considerations and suggestions are given when applying the measurement function to quality evaluation. Finally, comparison with different methods of quantifying quality characteristics and sub-characteristics with the measured values is analyzed.

Aiming at the disunion of the software quality standards and the difficulty to measure, a method for software quality evaluation based on fuzzy mathematics is put forward. According to the characteristic of the software quality model ISO9126, a general-purpose evaluation model for software quality is built by using the multilayer fuzzy comprehensive evaluation approach, to digitize the software quality evaluation. It is applied to evaluate the quality of the office software. The result of the experiment shows the feasibility of the software quality evaluation system based on multilayer fuzzy comprehensive evaluation.

This article describes empirical research results regarding the "history effect" in software quality evaluation processes. Most software quality models and evaluation processes models assume that software quality may be deterministically evaluated, especially when it is evaluated by experts. Consequently, software developers focus on the technical characteristics of the software product. A similar assumption is common in most engineering disciplines. However, in regard to other kinds of goods, direct violations of the assumption about objective evaluation were shown to be affected by the consequences of cognitive processes limitations. Ongoing discussion in the area of behavioral economics raises the question: are the experts prone to observation biases? If they are, then software quality models overlook an important aspect of software quality evaluation. This article proposes an experiment that aims to trace the influence of users' knowledge on software quality assessment. Measuring the influence of single variables for the software quality perception process is a complex task. There is no valid quality model for the precise measurement of product quality, and consequently software engineering does not have tools to freely manipulate the quality level for a product. This article proposes a simplified method to manipulate the observed quality level, thereby making it possible to conduct research. The proposed experiment has been conducted among professional software evaluators. The results show the significant negative influence (large effect size) of negative experience of users on final opinion about software quality regardless of its actual level.

‘Green’ is the magic word in today’s world. Many different fuels and fuel motors are being developed, assuming they will be adopted and used in the future as an alternative to fossil-fuel-based technologies. But many questions still remain unanswered. Is there going to be a supply problem? Is the ‘Running out of fossil fuels’ scenario merely a hype? Do we have any green alternative that can be adopted on a mass scale? Is there any alternative fuel capable of giving all the benefits of current fossil fuels and simultaneously eliminating pollution and price problems? And many more such questions! Alternative fuel development research arose from the perennial uncertainty in oil prices. This chapter attempts to study the reasons behind oil price fluctuations, believing them to be the root of the problem. Then the Analytic Hierarchy Process (AHP) model is presented. The AHP model has different levels, like a tree, the lowest level displaying all the alternatives to the problem. Each type of alternative motor fuel is briefly explained. Issues such as cost and the pollution impact, are assessed for each. Then, in the next level of the AHP model, the criteria are presented. Criteria include the different factors that have an impact on the adoption of each alternative. Economic, cultural, environmental, sustainability and development time criteria are considered. Finally, in the uppermost level of the AHP tree model is the goal: to find the best motor fuel for the future. To prove the robustness of each alternative fuel, four different scenarios are used, and quantitative results for each scenario are presented. It is hoped that the quantified values for each alternative might give us an idea of where the solution lies.

Most of the techniques related to part family formation require precise numerical data for part's features. However, in many situations, the parts features are too ill-defined and are too susceptible to analysis by traditional discrete methods. Fuzzy set theory allows the parts to belong to different groups with different memberships. Moreover, different attributes may not have equal importance. The AHP (analytical hierarchy process) model gives the weightage for different attributes and also checks the consistency of the judgements. This paper suggests two models for part family formation using fuzzy mathematics and the AHP model. One model is based on fuzzy logic and AHP and the second model uses fuzzy equivalence ratios and AHP. Models can be integrated with the existing coding systems used by organizations. Both models are validated using problems from literature.

Quality measured and evaluated based on organization-specific quality models cannot be compared to the quality of other software products. To alleviate this problem, ISO/IEC defined international standards called the SQuaRE (Systems and software Quality Requirements and Evaluation) series for comprehensive quality measurement and evaluation, however, these standards include ambiguous measurements, making them difficult to apply. Herein an initial comprehensive quality measurement framework, which includes a clear measurement plan based on ISO/IEC 25022 and 25023, is proposed. A case study confirms the usefulness of the framework. As future work, we will introduce the framework to various domains. And then, we revise and refine measurements and evaluation plans to improve feasibility and usefulness.

Mid-long term load forecasting is a critical component in power system planning and operation. Aiming at the characteristic of it bring forward a comprehensive model basing on three target quantities which involve target gross quantity and target increase quantity and target increase rate. At first construct an analytic hierarchy process (AHP) model to analyze and appraise three target quantities dividedly, and pick up best prediction models for them. And then synthesize three selected models' forecasting results to the final prediction result using radial basic function neural network (RBFNN), whose input data involve GDP of forecasting object in addition. AHP model integrates the forecasting error and fitting accuracy of models and introduces man's influence successful which are expert trusting of models and trend reliability of model forecasting results. AHP model analyzes history data which are most nearby to the forecasting time and therefore is better real-time. Experiment results show that the comprehensive model is provided with high forecasting precision and achieves satisfactory results in application.

This present study primarily focuses on the susceptibility analysis of the settlement area and metalled road based on the susceptibility index maps/susceptibility zonation maps. Susceptibility index maps were prepared using thee recognized techniques, i.e., frequency ratio (FR), analytical hierarchy process (AHP), and logistic regression (LR) models. Elevation, slope angle, slope aspect, slope curvature, stream power index (SPI), topographic wetted index/compound topographic index (TWI/CTI), land use/land cover (LULC), normalized differential vegetation index (NDVI), lineament number density, distance from lineament (D2L), drainage density (DD), distance to drainage (D2D), geology, soil, and rainfall were incorporated for the preparation landslide susceptibility zonation maps. The prediction accuracy of three aforesaid models was 93.6, 89.3, and 94.7%, respectively. The result indicated that the total settlement area and metalled road lengths were 48.84 km2 and 314.38 km, respectively, within basin boundary. Settlement areas of 33.11, 35.28, and 60.39% were located on the very high susceptibility zones based on the FR, AHP, and LR models, respectively. Similarly, 57.61, 17.25, and 3.08% metalled road out of total road length were found on the very high susceptibility zones based on the aforesaid three models, respectively. Total 130 mouzas fall within the basin boundary; out of these, the most vulnerable mouza was Bara Adalpur Dwitiya Khanda under Matigara block because 100% of the settlement area falls in the category of very high susceptibility zones on the basis of FR model. Dhajea Tea Garden and Manjua Forest mouza under Jorebunglow Sukiapokhri and Mirik block, respectively, were most susceptible to landslide hazard in respect of road based on the AHP model. The findings of the research can be utilized to prioritize risk management so that the losses and damages of properties and lives can be minimized due to future landslide event.

By the effect of globalization, products, services, capital, technology, and people began to circulate more freely in the world. As a conclusion, in order to achieve and gain an advantage against competitors, manufacturing firms had to adopt themselves to changing conditions and evaluate their critical performance criteria. In this study, the aim is to determine general performance criteria and their characteristics and classifications from previous studies and evaluate performance criteria for a plastic packaging organization by utilizing analytic hierarchy process (AHP) modeling. A specific manufacturing organization, operating in the Turkish plastic packaging sector has been selected and the manufacturing performance criteria have been determined for that specific organization. Finally, the selected criteria have been assessed according to their relative importance by utilizing AHP approach and expert choice (EC) software program. As a result of this study, operating managers chose cost, quality, customer satisfaction and time factors as criteria for this organization. As the findings of the study indicate, the manufacturing organization operating in the plastic packaging sector, overviews its operations and measures its manufacturing performance basically on those four criteria and their sub criteria. Finally, relative importance of those main measures and their sub criteria are determined in consideration to plastic packaging sector.

Water resources are under tremendous pressure as a result of the growing demand for water to meet human needs. Hence, it is necessary to delineate groundwater potential zones (GWPZs) to sustainably develop and manage groundwater resources. In this study, the geospatial-based analytical hierarchy process (AHP) and frequency ratio (FR) techniques were used to identify the GWPZs. Seven factors (geology, rainfall, slope, lineament density, soil, drainage density, and land use/land cover), which partially or entirely influence the groundwater potentiality of an area, were accessed separately and later combined to create GWPZ maps. Weights and ranks were assigned to the factors to perform the AHP model using existing knowledge. The FR was performed by calculating the percentage ratio between the dependent variable (boreholes) and the independent variable (factors). The preparation of the contributing factors and the creation of the resulting models was done using ArcGIS 10.8. The final GWPZ maps were classified into five zones: very low, low, moderate, high, and very high. About 80 boreholes in the study area were randomly subset into training and testing datasets; 58% were used for model training, and the remaining 42% were used for validation purposes. The receiver operating characteristic (ROC) curves for the GWPZs models were generated, and the areas under the curves (AUC) were calculated. Validation of the models shows that the FR model is more efficient (85.3% accuracy) than the AHP model (83.2% accuracy). The findings show that the AHP and FR models are reliable and can be adopted to characterize GWPZs in arid or semi-arid environments.

Bees and other important animals are vital to human survival on the earth. They not only produce delicious honey, but also play an indispensable role in plant pollination, making a unique contribution to the world. The bee population reduction crisis is known as a global problem of sustainable development goals. To consider the factors that can influence the size of honey bee populations and how these factors can affect the size of honey bee populations, we will use the most basic evaluation model. Given the need to accurately calculate the weight of each factor, we use the Analytic Hierarchy Process (AHP) model. In order to analyze the results of the AHP model, we will list our variable factors, apply the AHP model and use sensitivity analysis on it. Also, to predict how many honeybees hives you will need to support pollination of an 81,000 square meters parcel of land containing crops that benefit from pollination. we consider to use Markov Chain Monte Carlo (MCMC) model, which can indicate a stationary distribution to the attendance rate of the bees after n times.