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ISO/IEC 9126 software quality model
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For any software, its evaluation is significant for managing, controlling so that we can improve a software development process. For such evaluation of software, many factors have been recognized in literature surveys. Quality is one of most important factor which cannot be measured easily, because of its dependency on various other factors. Usabil...
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... for the selection of quality characteristics which can be divided further into different sub-characteristics. ISO/IEC 9126 introduces a base model which classifies software quality into six categories: portability, usability, maintainability reliability, efficiency, and functionality which are further divided into measurable sub-characteristics ( Fig. ...
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Citations
... Another mobile usability attribute discussed by many researchers is Errors [64], [67], [68]. In general, it refers to problems that a user encounters while using mobile applications. ...
Mobile devices have become an integral part of the digital ecosystem, connecting people, businesses, and information around the world in ways never before possible. In particular, smartphones, tablets and other handheld devices equipped with mobile applications have changed every aspect of our lives. Today, a user can choose from nearly five million applications available for both Android and iOS operating systems. However, only 0.5 percent of applications succeed in the marketplace. Many factors contribute to their failure, including poor design, lack of value, privacy violations, and usability issues. While usability is often identified as a major concern, there seems to be no agreement between researchers and practitioners on its nature, although many models have been developed. This paper attempts to find a consensus by synthesizing the state of the art literature. More specifically, we aim to develop a consolidated, universal usability model for mobile applications, through the lens of existing human computer interaction theory. In order to achieve this goal, our study uses a mix of qualitative and quantitative methods. Overall, the research methodology consisted of two steps. First, we conducted a systematic literature review to identify, collect, and analyze current research on mobile usability. Second, we used the meta-analysis approach to quantitatively describe the extracted data and summarize the findings. The PACMAD+3 model was developed and discussed in light of the results obtained and the PACMAD model. While our model borrows seven attributes from its ancestor, the remaining three attributes were derived from the synthesis of other studies, along with three external factors adopted from the ISO 9241-11 standard. In addition, we reviewed existing definitions of usability attributes. We expect that this unified approach will lead to a better understanding of mobile usability, including all relevant attributes and factors, thus making a significant contribution to theory. On the other hand, in practice, the PACMAD+3 model can be used to translate abstract attributes into tangible terms, which is particularly useful in empirical research focused on measuring and evaluating the usability of mobile applications.
... Quality attributes can be composed of other sub-attributes, for example portability can be broken down into adaptability, installability, replaceability [41]. Sub-attributes contribute and combine to achieve a particular quality attribute goal. ...
We propose a method to specify and evaluate the trustworthiness of AI-based systems using scenarios and design tactics. Using our trustworthiness scenarios and design tactics, we can analyze the architectural design of AI-enabled systems to ensure trustworthiness has been properly achieved. Trustworthiness scenarios allow for the specification of trustworthiness, and design tactics are used to achieve the desired level of trustworthiness in the system. We illustrate the validity of our proposal through the design of the software architecture of a pollination robot. We find that our method opens discussions on ways to achieve trustworthiness and leads to the discovery of any weaknesses in the design concerning the trustworthiness of the AI system. Furthermore, our method allows for designing an AI system with trustworthiness in mind and therefore leads to greater analysis and identification of the sub-attributes that affect the trustworthiness of an AI system.KeywordsTrustworthinessTrustworthy AIUtility treeTrustSoftware architectureQuality attribute scenariosArchitectural tacticsArchitecture analysisATAM
... The International Standards Organization (ISO) (2019), defines usability in the ISO norm FDIS 9241-210 as 'the extent to which a system, product or service can be used by a particular user to achieve a specific goal effectively, efficiency and satisfaction/control in a particular context of use'. Gupta et al. (2014) extend the definition of ISO 9241-11 by adding attributes of ease of learning and safety in usability. Then, the quality of the content is measured using The Bernier Instructional Design Scale (BIDS) which serves as a standard or blueprint for the quality of instructional design. ...
This research aimed to examine the effect of user experience on Pigijo’s website www.pigijo.com on Pigijo customer satisfaction by using an experimental study and comparing between two different groups of user behavior, namely goal-directed and experiential. The experiment was conducted using an accidental purposive sampling technique on 60 respondents divided into two groups based on user behavior. The data collection of this experiment used a questionnaire that had been tested for validity and reliability, then carried out various tests and analyses such as descriptive analysis, t-test, and regression tests. As a result of the research. It was found that the user experience on the www.pigijo.com website has a positive relationship and influence on Pigijo customer satisfaction, where the goal-directed group has a higher average rating (69.40%) and affects customer satisfaction more positively compared to the experiential group (65.50%).
... Most of these algorithms use fitness function calculation for optimizing problems [24][25][26]. Various evolutionary algorithms which have been used in the past for feature selection are grey wolf optimization [11], bat algorithm [3], chaotic crow search algorithm [4], whale optimization algorithm [5], genetic algorithms [6], cuckoo search [7], and recently studied MMFO [37]. ...
Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.
... The main motive of the presented work is to define usability in terms of ''hierarchical-based software usability model'' [4]. The function of the hierarchical-based model is to combine the usability factors, attributes, and its characteristics in non-redundant approach. ...
... This model consists of seven factors which represents attributes/features hierarchically. The factors, attributes, and characteristics have been combined using the three levels in the hierarchical model [4]. The first level of hierarchy model defines usability factors, the second level of hierarchy defines the usability attributes/features, and the third level of hierarchy defines the usability characteristics. ...
... By training the evaluation set, the presence or absence of the feature is depicted. The private usability dataset described in [4] has been used in this work for usability feature selection using the new proposed metaheuristic algorithm. ...
For the purpose of usability feature extraction and prediction, an innovative metaheuristic algorithm is introduced. Generally, the term “usability” is defined by the several researchers with respect to the hierarchical-based software usability model and it has become one of the important methods in terms of software quality. In hierarchically based software, its usability factors, attributes, and its characteristics are combined. The paper presented an algorithm, i.e., modified crow search algorithm (MCSA) mainly for extraction of usability features from hierarchical model with the optimal solution under the search for useful features. MCSA is an extension of original crow search algorithm (CSA), which is a naturally inspired algorithm. The mechanism of this algorithm is based on the process of hiding food and prevents theft and hence introduced this CSA in the field of software engineering practices as an inspiration. The algorithm generates a particular number of selected features/attributes and is applied on software development life cycles models, finding out the best among them. The results of the presented algorithm are compared with the standard binary bat algorithm (BBA), original CSA, and modified whale optimization algorithm (MWOA). The outcomes conclude that the proposed MCSA performs well than the standard BBA and original CSA as the proposed algorithms generate fewer number of feature selection equal to 17 than 18 in BBA, 23 in CSA, and 19 in MWOA.
... • This work uses the term 'usability' in the context of software development using hierarchical model, which has been defined by the authors in their existing work [26]. • A modified nature-inspired optimized algorithm is being presented for selection of usability features. ...
... The model has seven sets of factors that are divided into features, which are further divided into characteristics in hierarchical manner [26]. The hierarchical based usability model is built by examining the existing taxonomy of usability and then constructing the features and their characteristics in hierarchical way. ...
... Thus making a hierarchical model that clears the confusion and inconsistency among the experts. Now these set of 23 features are used to prepare evaluation dataset [26]. This private usability dataset has been used for usability feature/attribute selection using the new proposed algorithm called Modified Moth-Flame Optimization algorithm. ...
This paper introduces a nature-inspired optimized algorithm called modified moth-flame optimization (MMFO) for usability feature selection. To determine quality of software usability plays a significant role. This model contains various usability factors that are divided into several features, which have some characteristics, thus making a hierarchical model. Here, the authors have introduced MMFO (Modified Moth-flame optimization algorithm) for the selection of usability features to get an optimal solution MMFO is an extension of moth-flame optimization algorithm (MFO), which is based on the navigation method of moths called transverse orientation and to the best of our knowledge; this algorithm is introduced in software engineering practices. The selected features and accuracy of proposed MMFO is compared with the original MFO and other related optimization techniques. The results shows that the proposed nature-inspired optimization algorithm outperforms the other related optimizers as it generates a fewer number of selected features and having low accuracy.
... For doing feature selection of software-based usability models [19], many bio-inspired algorithms had been developed viz. modified crow search algorithm [20], modified binary bat algorithm [10] and modified whale optimization algorithm [21]. ...
The Modified Grasshopper Optimization Algorithm which identifies Parkinson disease symptoms at an early (premature) stage was proposed. Parkinson disease, type of movement ailment, could be life-threatening if not treated at premature stage. Therefore, diagnosis of Parkinson disease became essential in early stages so that all the symptoms could be controlled by giving required medication to the patient. Hence ensuring the patient longevity. As part of this research work, a novel model Modified Grasshopper Optimization Algorithm was introduced which was based on the traditional Grasshopper Optimization Algorithm and search strategy for feature selection. Grasshopper Optimization Algorithm was relatively a novel heuristic optimization swarm intelligence algorithm which was stimulated by grasshoppers searching for food. This population-based method has capability to provide solution for real-life problems in undefined search space. It mimics grasshopper swarm’s behaviour and their social interaction. Popular algorithms like Random Forest, Decision Tree and k-Nearest Neighbour classifier were used in judgement on shortlisted aka selected features. Different datasets of handwriting (meander and spiral), speech and voice were used for evaluating the presented model. The proposed algorithm was effective in Parkinson disease identification having accuracy (computed) of 95.37%, 99.47% detection rate and 15.78% false alarm rate. This helps larger cause of patient in receiving treatment in pre-mature stage. The presented bio-inspired algorithm was adequately steady and has ability to identify the optimal feature set. Finally results obtained from the assessment of introduced Modified Grasshopper Optimization Algorithm on these data sets were evaluated and contrasted with respect to outcome of Modified Grey Wolf Optimizer and Optimized Cuttlefish Algorithm. The experiment’s outcome revealed that the presented Modified Grasshopper Optimization Algorithm assists in reducing the selected features count and improving the accuracy.
... Live auction and SDLC datasets have been discoursed in [13]. A number of bio-inspired algorithms have been proposed and implement for effective feature of software-based usability models [12] and can be used for prediction of seasonal crops like modified crow search algorithm [14], modified binary bat algorithm [9] and modified whale optimization algorithm [21]. Thyroid disease optimal features have been identified using grey wolf optimization [32]. ...
Agriculture plays a significant role in the growth and development of any nation’s economy. But, the emergence of several crop-related diseases affects the productivity in the agriculture sector. To cope up this issue and to make aware the farmers to prevent the expansion of diseases in crops and to implement effective management, crop disease diagnosis plays its significant role. Researchers had already used many techniques for this purpose, but some vision-related techniques are yet to be explored. Commonly used techniques are support vector machine, k-means clustering, radial basis functions, genetic algorithm, image processing techniques like filtering and segmentation, deep structured learning techniques like convolutional neural network. We have designed a hybrid approach for detection of crop leaf diseases using the combination of convolutional neural networks and autoencoders. This research paper provides a novel technique to detect crop diseases with the help of convolutional encoder networks using crop leaf images. We have obtained our result over a 900-image dataset, out of which 600 constitute the training set and 300 test set. We have considered 3 crops and 5 kinds of crop diseases. The proposed network was trained in such a way that it can distinguish the crop disease using the leaf images. Different convolution filters like 2 × 2 and 3 × 3 are used in proposed work. It was observed that the proposed architecture achieved variation in accuracy for the different number of epochs and for different convolution filter size. We reached 97.50% accuracy for 2 × 2 convolution filter size in 100 epochs, while 100% accuracy for 3 × 3 filter size which is better than other conventional methods.
... Their model is based on the premise that majority of the existing models lack the capacity to adequately cover all usability aspects. The proposed model adopts a hierarchical structure and serves to accomplish the evaluation criteria based on 7 key attributes and 23 sub-attributes [5]. Singh and Kumar describe a web evaluation approach aimed at assessing the usability of live websites using two different levels of measurement. ...
Website usability is one of the most important quality factors which cannot be measured easily, because of its dependency on various other factors, which some of them are difficult to be measured. Literature shows several website usability models which do not include all usability aspects and shows the difficulty of measuring usability. This paper proposes a website hierarchical usability model with 9 major factors and 24 measurable criteria which are distributed and replicated among factors in hierarchical manner to achieve weight concept. Also this paper introduces a case study Jordan University Website with free tools to measure its usability.
... In ecommerce market, a paper uses ideal features for ranking analysis of online customer product reviews using opinion mining with clustering [20]. The hierarchical software usability model has been designed using fuzzy expert system [21,22,23] to predict the usability of software development life cycle models D. Gupta et al., [24] and live auction portal [25,26]. The datasets for SDLC and Live auction have been discussed in D. Gupta, A. Khanna [27]. ...
In every aspect of human life, sound plays an important role. From personal security to critical surveillance, a sound is a key element to develop automated systems for these fields. Few systems are already in the market but their efficiency is a concerned point for their implementation in real life scenarios. The learning capabilities of Deep learning architectures can be used to develop sound classification systems to overcome efficiency issues of traditional systems. Our aim in this paper is to use deep learning networks for classifying environmental sounds based on the generated spectrograms of these sounds. We used spectrogram images of environmental sounds to train Convolutional neural network (CNN) and Tensor Deep Stacking Network (TDSN). We used two datasets for our experiment, ESC-10 and ESC-50. Both systems were trained on these datasets and the achieved accuracy was: 1) 77% and 49% in CNN 2) 56% in TDSN trained on ESC 10. From this experiment, it is concluded that proposed approach for sound classification using spectrogram images of sounds can be efficiently used to develop sound classification and recognition systems.
