Usability is a key quality attribute of successful software systems. Unfortunately, there is no common understanding of the factors influencing usability and their interrelations. Hence, the lack of a comprehensive basis for designing, analyzing, and improving user interfaces. This paper proposes a 2-dimensional model of usability that associates system properties with the activities carried out by the user. By separating activities and properties, sound quality criteria can be identified, thus facilitating statements concerning their interdependencies. This model is based on a tested quality meta-model that fosters preciseness and completeness. A case study demonstrates the manner by which such a model aids in revealing contradictions and omissions in existing usability standards. Furthermore, the model serves as a central and structured knowledge base for the entire quality assurance process, e.g. the automatic generation of guideline documents.
There are approaches which aim to consolidate the different models. Seffah et al. @cite_22 applied the FCM model to the quality attribute . The developed model contains 10 factors which are subdivided into 26 criteria. For the measurement of the criteria the model provides 127 metrics.
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Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the project Quamoco, we built a comprehensive approach with the aim to close this gap. For this, we developed in several iterations a meta quality model specifying general concepts, a quality base model covering the most important quality factors and a quality assessment approach. The meta model introduces the new concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects. Product factors have measures and instruments to operationalise quality by measurements from manual inspection and tool analysis. The base model uses the ISO 25010 quality attributes, which we refine by 200 factors and 600 measures for Java and C# systems. We found in several empirical validations that the assessment results fit to the expectations of experts for the corresponding systems. The empirical analyses also showed that several of the correlations are statistically significant and that the maintainability part of the base model has the highest correlation, which fits to the fact that this part is the most comprehensive. Although we still see room for extending and improving the base model, it shows a high correspondence with expert opinions and hence is able to form the basis for repeatable and understandable quality assessments in practice. Recommender problems with large and dynamic item pools are ubiquitous in web applications like content optimization, online advertising and web search. Despite the availability of rich item meta-data, excess heterogeneity at the item level often requires inclusion of item-specific "factors" (or weights) in the model. However, since estimating item factors is computationally intensive, it poses a challenge for time-sensitive recommender problems where it is important to rapidly learn factors for new items (e.g., news articles, event updates, tweets) in an online fashion. In this paper, we propose a novel method called FOBFM (Fast Online Bilinear Factor Model) to learn item-specific factors quickly through online regression. The online regression for each item can be performed independently and hence the procedure is fast, scalable and easily parallelizable. However, the convergence of these independent regressions can be slow due to high dimensionality. The central idea of our approach is to use a large amount of historical data to initialize the online models based on offline features and learn linear projections that can effectively reduce the dimensionality. We estimate the rank of our linear projections by taking recourse to online model selection based on optimizing predictive likelihood. Through extensive experiments, we show that our method significantly and uniformly outperforms other competitive methods and obtains relative lifts that are in the range of 10-15 in terms of predictive log-likelihood, 200-300 for a rank correlation metric on a proprietary My Yahoo! dataset; it obtains 9 reduction in root mean squared error over the previously best method on a benchmark MovieLens dataset using a time-based train test data split. We propose a novel unified recommendation model, URM, which combines a rating-oriented collaborative filtering (CF) approach, i.e., probabilistic matrix factorization (PMF), and a ranking-oriented CF approach, i.e., list-wise learning-to-rank with matrix factorization (ListRank). The URM benefits from the rating-oriented perspective and the ranking-oriented perspective by sharing common latent features of users and items in PMF and ListRank. We present an efficient learning algorithm to solve the optimization problem for URM. The computational complexity of the algorithm is shown to be scalable, i.e., to be linear with the number of observed ratings in a given user-item rating matrix. The experimental evaluation is conducted on three public datasets with different scales, allowing validation of the scalability of the proposed URM. Our experiments show the proposed URM significantly outperforms other state-of-the-art recommendation approaches across different datasets and different conditions of user profiles. We also demonstrate that the primary contribution to improve recommendation performance is contributed by the ranking-oriented component, while the rating-oriented component is responsible for a significant enhancement. The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd.