Abstract Cloud services operate in highly dynamic and heterogeneous environments, requiring continuous and accurate assessment of service quality. While Quality of Service (QoS) models are widely used to monitor performance, deep learning (DL) architectures–such as Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Units (BI-GRU)–offer enhanced capabilities for forecasting potential Service Level Agreement (SLA) violations. However, many existing experiments in this domain suffer from methodological shortcomings, including the use of outdated or proprietary datasets, a narrow set of QoS metrics, incomplete documentation of model architectures and training procedures, and a lack of statistical rigor, which undermines reproducibility and applicability in industrial contexts. This study empirically compares the performance of BI-GRU, LSTM, and AutoRegressive Integrated Moving Average (ARIMA) models for QoS forecasting using a rigorously designed experimental protocol that addresses these limitations. We build a multi-metric QoS dataset covering five months of operational data from a cloud service in an IT company, comprising 16 QoS metrics. Forecasting models were trained and evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with training time considered as an efficiency indicator. BI-GRU outperformed ARIMA across all QoS metrics and achieved statistically significant improvements over LSTM in 9 out of 16 metrics. In contrast, LSTM only significantly outperformed BI-GRU in one metric. Our findings demonstrate that most BI-GRU models provide superior accuracy and efficiency. Furthermore, the methodological rigor of the experimental design supports their applicability for proactive QoS management and informed decision-making in industrial cloud service environments.

Adaptive User Interfaces (AUI) play a crucial role in modern software applications by dynamically adjusting interface elements to accommodate users’ diverse and evolving needs. However, existing adaptation strategies often lack real-time responsiveness. Reinforcement Learning (RL) has emerged as a promising approach for addressing complex, sequential adaptation challenges, enabling adaptive systems to learn optimal policies based on previous adaptation experiences. Although RL has been applied to AUIs,integrating RL agents effectively within user interactions remains a challenge.

Abstract Context Adapting the User Interface (UI) of software systems to users’ requirements and their context of use is a challenging task. It involves determining the right adaptation, at the right time and place, to make it valuable for end-users. We believe that recent progress in Machine Learning (ML) techniques could provide useful ways in which to support adaptation more effectively. In particular, Reinforcement Learning (RL) has proven to be effective in planning a sequence of UI adaptations over a long time horizon. However, RL requires either manually specifying a reward function or learning a reward model. Currently there is no empirical evidence supporting the usefulness of reward models for UI adaptation. Objective This paper presents a confirmatory empirical study aimed at investigating the effectiveness of two different approaches to generating reward models in the context of UI adaptation using reinforcement learning: (1) a reward model derived exclusively from predictive Human-Computer Interaction (HCI) models (AUI-HCI), and (2) a reward model derived from predictive HCI models augmented by human feedback (AUI-HCI-HF), compared to non-adaptive (NA) interfaces. Method A controlled experiment with an AB/BA crossover design was conducted to evaluate the impact of these reward models on user experience, measured through objective and subjective engagement, as well as user satisfaction. Our study contributes to the understanding of how reward modeling can facilitate UI adaptation through RL. Results The results showed a significant improvement in objective engagement for AUI-HCI-HF compared to non-adaptive interfaces. However, no significant differences were found between AUI-HCI and non-adaptive interfaces for any of the other measurements, across any conditions. Conclusion Integrating human feedback into RL reward models enhances objective engagement, but its impact on subjective engagement and user satisfaction remains limited. While AUI-HCI-HF shows promise for improving interaction metrics, further research is needed to better align reward models with broader user perceptions and preferences, particularly compared to non-adaptive interfaces.

Abstract One of the important issues in social networks is the social communities which are formed by interactions between its members. Three types of community including overlapping, non-overlapping, and hidden are detected by different approaches. Regarding the importance of community detection in social networks, this paper provides a systematic mapping of machine learning-based community detection approaches. The study aimed to show the type of communities in social networks along with the algorithms of machine learning that have been used for community detection. After carrying out the steps of mapping and removing useless references, 246 papers were selected to answer the questions of this research. The results of the research indicated that unsupervised machine learning-based algorithms with 41.46% (such as k means) are the most used categories to detect communities in social networks due to their low processing overheads. On the other hand, there has been a significant increase in the use of deep learning since 2020 which has sufficient performance for community detection in large-volume data. With regard to the ability of NMI to measure the correlation or similarity between communities, with 53.25%, it is the most frequently used metric to evaluate the performance of community identifications. Furthermore, considering availability, low in size, and lack of multiple edge and loops, dataset Zachary’s Karate Club with 26.42% is the most used dataset for community detection research in social networks.

Context. Several early requirements approaches focus on modeling objectives, interest or benefits of related stakeholders. However, as they can be used for different purposes as identifying problems, exploring system solutions, evaluating alternatives, etc., there are no clear guidelines on how to build these models, which constructs of the language must be used in each case, and most importantly, how to use these models downstream to the software requirements and design artifacts. Background. In a previous work, we proposed a specialization of the GRL language ([email protected]) to specify stakeholders' goals when dealing with early requirements in the context of incremental software development. Goal/Method. This paper reports on a controlled experiment aimed at comparing the goal model quality and the productivity, perceived ease of use, and perceived usefulness of participants when using [email protected] and i* languages. Results. The results showed that [email protected] obtained better results than i* as a goal modeling language indicating that it can be considered as a promising emerging approach in this area. Conclusions. [email protected] allows obtaining goal models with good quality that may be later used downstream software development activities.

There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.

The International Software Benchmarking Standards Group (ISBSG) maintains a repository of data about completed software projects. A common use of the ISBSG dataset is to investigate models to estimate a software project's size, effort, duration, and cost. The aim of this paper is to determine which and to what extent variables in the ISBSG dataset have been used in software engineering to build effort estimation models. For that purpose a systematic mapping study was applied to 107 research papers, obtained after a filtering process, that were published from 2000 until the end of 2013, and which listed the independent variables used in the effort estimation models. The usage of ISBSG variables for filtering, as dependent variables, and as independent variables is described. The 20 variables (out of 71) mostly used as independent variables for effort estimation are identified and analysed in detail, with reference to the papers and types of estimation methods that used them. We propose guidelines that can help researchers make informed decisions about which ISBSG variables to select for their effort estimation models.

Background: The International Software Benchmarking Standards Group (ISBSG) dataset makes it possible to estimate a project's size, effort, duration, and cost.

The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with over 6000 software projects. This dataset makes it possible to estimate a project’s size, effort, duration, and cost. The aim of this study was to determine how and to what extent, ISBSG has been used by researchers from 2000, when the first papers were published, until June of 2012. A systematic mapping review was used as the research method, which was applied to over 129 papers obtained after the filtering process. The papers were published in 19 journals and 40 conferences. Thirty-five percent of the papers published between years 2000 and 2011 have received at least one citation in journals and only five papers have received six or more citations. Effort variable is the focus of 70.5% of the papers, 22.5% center their research in a variable different from effort and 7% do not consider any target variable. Additionally, in as many as 70.5% of papers, effort estimation is the research topic, followed by dataset properties (36.4%). The more frequent methods are Regression (61.2%), Machine Learning (35.7%), and Estimation by Analogy (22.5%). ISBSG is used as the only support in 55% of the papers while the remaining papers use complementary datasets. The ISBSG release 10 is used most frequently with 32 references. Finally, some benefits and drawbacks of the usage of ISBSG have been highlighted. This work presents a snapshot of the existing usage of ISBSG in software development research. ISBSG offers a wealth of information regarding practices from a wide range of organizations, applications, and development types, which constitutes its main potential. However, a data preparation process is required before any analysis. Lastly, the potential of ISBSG to develop new research is also outlined.

Background: Bayesian networks have been applied in many fields, including effort estimation in software engineering. Even though there are Bayesian inference algorithms than can handle continuous variables, performance tends to be better when these variables are discretized that when they are assumed to follow a specific distribution. On the other hand, the choice of the discretization method and the number of discretized intervals may lead to significantly different estimating results. However, discretization issues are seldom mentioned in software engineering effort estimation models.

There are many quantitative estimation methods, e.g. linear regression, neural networks, regression trees. Compared to traditional methods, Bayesian networks are being increasingly used in software engineering because their use opens many possibilities. A main feature of Bayesian networks is their capability to combine data and expert knowledge. This paper seeks to reinforce the hypothesis that Bayesian networks are a competitive method for estimating software effort in terms of prediction accuracy. For this purpose a Naive Bayes Classifier (NBC) and a forward Stepwise Regression (SWR) models have been developed from a subset of the ISBSG dataset. Under homogeneous conditions we found similar results provided that the discretization of the continuous variables is thin enough.

With the increasingly security problems in networks and systems, the evolution of development models that underlie current tools and techniques is all that is required to produce a model of self-defense where all the components are self-protected. The great extension of current models recommends a reasonable transition fundamentally based on the family of standards ISO/IEC 31000, where the selection of the appropriate risk assessment techniques revolves around the complexity X of the system or process structure and the uncertainty Y of the environment. The consistency and sufficiency of both factors X Y, demonstrated using the Information Theory, can explain the risk of a dynamic system. However, going deep into their meaning, the Theory of Calculability should ensure its validity in the design of self-protected networks and dynamic systems. This design needs to be chained to the implementation of networks and self-protected components, using a new type of algorithmic languages. The authors propose the outline of such a development model expected to focus and better address the lack of current security.