Advanced analytical techniques and sophisticated decision-making strategies are imperative for handling extensive volumes of data. As the quantity, diversity, and speed of data increase, there is a growing lack of confidence in the analytics process and resulting decisions. Despite recent advancements, such as metadata mechanisms in Big Data Processing and Systems of Deep Insight, effectively managing the vast and varied data from diverse sources remains a complex and unresolved challenge. Aiming to enhance interaction with Data Lakes, this paper introduces a framework based on a specialized semantic enrichment mechanism centred around data blueprints. The proposed framework takes into account unique characteristics of the data, guiding the process of locating sources and retrieving data from Data Lakes. More importantly, it facilitates end-user interaction without the need for programming skills or database management techniques. This is performed using Digital Twin functionality which offers model-based simulations and data-driven decision support.

In the continuously evolving and growing landscape of Big Data, a key challenge lies in the transformation of a Data Lake into a Data Mesh structure. Unveiling a transformative approach through semantic data blueprints enables organizations to align with changing business needs swiftly and effortlessly. This paper delves into the intricacies of detecting and shaping Data Domains and Data Products within Data Lakes and proposes a standardized methodology that combines the principles of Data Blueprints with Data Meshes. Essentially, this work introduces an innovative standardization framework dedicated to generating Data Products through a mechanism of semantic enrichment of data residing in Data Lakes. This mechanism not only enables the creation readiness and business alignment of Data Domains, but also facilitates the extraction of actionable insights from software products and processes. The proposed approach is qualitatively assessed using a set of functional attributes and is compared against established data structures within storage architectures yielding very promising results.

One of the greatest challenges in Smart Big Data Processing nowadays revolves around handling multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data through Data Lakes. The latter requires a disciplined approach to collect, store and retrieve/analyse data to enable efficient predictive and prescriptive modelling, as well as the development of other advanced analytics applications on top of it. The present paper addresses this highly complex problem and proposes a novel standardization framework that combines mainly the 5Vs Big Data characteristics, blueprint ontologies and Data Lakes with ponds architecture, to offer a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval from a Data Lake. The proposed mechanism is compared qualitatively against existing metadata systems using a set of functional characteristics or properties, with the results indicating that it is indeed a promising approach.

The IoT ecosystem is evolving quickly, developing several applications in different sectors. The majority of these applications use centralized infrastructures something that poses several challenges especially related to trust and data security. Recently, blockchain has been introduced as an effective solution to IoT applications trying to provide solutions to these challenges. In this paper, we introduce a new decentralized IoT application using blockchain technology, namely, ParkChain. The ParkChain application operates in a way that no one can delete, revert, hack or question the time a registered vehicle securely entered a parking area. In order to evaluate ParkChain, we have implemented the smart contract on top of Ethereum Blockchain along with a traditional Image Processing / Computer Vision approach for License Plate Recognition, and using a Raspberry Pi we control the access on a parking place. We report on several experiments that assess the performance of ParkChain application.

Nowadays more and more software companies, as well as individual software developers, adopt the microservice architecture for their software solutions. Although many software systems are being designed and developed from scratch, a significant number of existing monolithic solutions tend to be transformed to this new architectural style. What is less common, though, is how to migrate component-based software systems to systems composed of microservices and enjoy the benefits of ease of changes, rapid deployment and versatile architecture. This paper proposes a novel and integrated process for the decomposition of existing software components with the aim being to fully or partially replace their functional parts with by a number of suitable and available microservices. The proposed process is built on semi-formal profiling and utilizes ontologies to match between properties of the decomposed functions of the component and those offered by microservices residing in a repository. Matching concludes with recommended solutions yielded by multi-objective optimization which considers also possible dependencies between the functional parts.

In this work, we attempt to ameliorate the impact of data sparsity in the context of supervised modeling applications dealing with high-dimensional sequential data. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed sequential data, so as to inform the predictive algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques.

This paper presents a novel approach to improve the accuracy of classification models used for prediction purposes by integrating a Fuzzy Cognitive Map (FCM) to produce a hybrid model. The proposed methodology first uses the FCM to discover latent correlations that exist between the data in order to form a single variable. This variable is then fed in the classification model as part of the training and testing phases to enhance its accuracy. Experimental results using datasets describing two different problems suggested noteworthy improvements in the accuracy of various classification models.

Abstract Software cost estimation is a key process in project management. Estimations in the initial project phases are made with a lot of uncertainty that influences estimation accuracy which typically increases as the project progresses in time. Project data collected during the various project phases can be used in a progressive time‐dependent fashion to train software cost estimation models. Our motivation is to reduce uncertainty and increase confidence based on the understanding of patterns of effort distributions in development phases of real‐world projects. In this work, we study effort distributions and suggest a four‐stage progressive software cost estimation model, adjusting the initial effort estimates during the development life‐cycle based on newly available data. Initial estimates are reviewed on the basis of the experience gained as development progresses and as new information becomes available. The proposed model provides an early, a post‐planning, a post‐specifications, and a post‐design estimate, while it uses industrial data from the ISBSG (R10) dataset. The results reveal emerging patterns of effort distributions and indicate that the model provides effective estimations and exhibits high explanatory value. Contributions in lessons learned and practical implications are also provided.

One of the most significant tasks of component-based software development is concerned with finding suitable components for integration. This paper introduces a novel development framework that promotes reusability and focuses on assessing the suitability level of candidate components. A specifications profile is first created using a semi-formal natural language that describes the desired functional and non-functional properties of the component(s) sought. A parser automatically recognizes parts of the profile and translates them into instance values of a dedicated CBSE ontology, the latter addressing issues of components' reusability. Available components on the market are also stored as instances of the CBSE ontology. Matching between required and offered component properties takes place automatically at the level of the ontology items and a suitability ratio is calculated that suggests which components to consider for integration.

As modern computing relies more and more on distributed solutions of services and resources over the cloud, the need of potential users to assess whether the transition from traditional software systems to the cloud would be to their benefit becomes even greater. Cloud vendors also seek ways to study beforehand the behavior of potential users with respect to their decision to adopt the cloud environment so as to take actions towards enhancing the positive side. Therefore, the study of the parameters forming the environment behind the cloud adoption decision is of paramount importance to both users and vendors. In this context the present paper proposes a multi-layer FCM approach which models a number of factors which play a decisive role to the cloud adoption issue and offers the means to study their influence. The factors are organized in different layers which focus on specific aspects of the cloud environment, something which, on one hand, enables tracking the causes for the decision outcome, and on the other offers the ability to study the dependencies between the leading determinants of the decision. The construction and analysis of the model is based on factors reported in the relevant literature and the utilization of experts' opinion. The efficacy and applicability of the proposed approach are demonstrated through four real-world experimental cases.

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

ABSTRACT The paper provides an in depth analysis of empirical evidence on the state of practice within the agile domain obtained through a survey conducted in 2012. The context of focus is agile software processes and teams and the particular topics of interest revolve around three axes: (i) communication; (ii) project management; and (iii) quality assurance and validation. The aim of the survey is to deliver the current levels of agile adoption and practices as these are recorded in the responses of professionals in IT services and the software industry. The goal of the survey is to provide evidence‐based assessment of the level of agile adoption by software development organizations, in relation to the general profile of the respondents (country of origin, business sectors, roles, etc.) and compared with different types of practices followed, such as agile techniques adopted, team organization and communication techniques, and project management. Particular patterns and trends are identified in the survey connecting the use of the agile paradigm with the aforementioned practices and investigating its relation with the roles of the respondents and the business strategies of their organizations. Copyright © 2014 John Wiley & Sons, Ltd.

Software project managers are often faced with challenges when trying to effectively staff and schedule projects. Incorrectly planning and estimating the execution of tasks frequently causes software projects to be delivered late and/or over budget, whereas not selecting the appropriate developers to carry out tasks may produce lower-quality, defective software products. To combat these challenges, this paper presents IntelliSPM -- a tool aiming to support software project management activities consisting of several optimization mechanisms borrowed from the area of Computational Intelligence. The tool takes into account technical aspects but also significant human factors, which have been found to play a crucial role in software quality and developer productivity. The purpose of IntelliSPM is to offer suggestions to project managers containing a set of possible project schedules and staffing strategies that minimizes duration and maximizes resource usage. Several simulated and real-world projects were used during the validation process, with results showing that IntelliSPM is capable of providing that much-needed practical benefit to software companies to improve various aspects of development, such as performance and job satisfaction, whilst keeping within the general objectives and particular constraints of each software project.

Software cost estimation (SCE) is one of the critical activities in software project management. During the past decades various models have been proposed for SCE. However, developing accurate and useful models is limited in practice despite the considerable financial gain they could offer to software stakeholders. Traditional techniques, such as regression, by-analogy and machine learning, face the difficulty of handling the dynamic nature of the software process and the problematic nature of the public data available. This paper addresses the issue of SCE proposing an alternative approach that combines robust decision tree structures with fuzzy logic. Fuzzy decision trees are generated using the CHAID and CART algorithms in a systematic manner, while development effort is treated as the dependent variable against two subsets of factors: The first contains selected attributes from the ISBSG, COCOMO and DESHARNAIS datasets and the second contains a subset of the available factors that can be measured early in the development cycle. The association rules obtained from the trees are then merged and defuzzified through a Fuzzy Implication System (FIS). The fuzzy framework is utilized to perform effort estimations. Experimental results indicate that the proposed approach is promising as it yields quite accurate estimations in most dataset cases considered. Finally, our evaluation suggests that accurate estimations may be produced, even when using only a small set of factors that can be measured early in the development cycle, thus increasing the practical value of the proposed cost model.

The lack of a software tool to automate the creation of a well-defined, Natural Language Software Requirements Specification (SRS) document is an obstacle to the process of efficient Requirements Engineering (RE). This paper provides an overview of a methodology and a novel software tool that attempt to formalize and automate the RE process, and it expands on the use of the SRS Documentation component of the tool that generates automatically a well-structured Natural Language SRS document.

This paper introduces a new algorithm for traversing and executing multilayered fuzzy cognitive maps (ML-FCMs) that aim to enhance this methodology, which is designed for handling complicated large scale problems. The methodology is based on the decomposition of the parameters of the problem under investigation into smaller quantities, organised in a hierarchical structure forming a multilayered FCM model. The present work aspires to eliminate the weaknesses of the existing ML-FCM algorithm, which reside in the way activation levels are calculated for those concepts decomposed into a set of parameters at lower layers in the map. The current algorithm calculates these levels by completing a full iteration cycle at the lower level thus losing the information produced between the iterative steps. We attempt to solve this problem by introducing the enhanced ML-FCM algorithm, (EML-FCM) which allows calculations in-between iterations and takes into consideration the change of activation levels in a more detailed form. The strong features of the proposed EML-FCM algorithm are presented and discussed, in addition to the provision of a comparison between the two algorithms.

This paper addresses the issue of software cost estimation through fuzzy decision trees, aiming at acquiring accurate and reliable effort estimates for project resource allocation and control. Two algorithms, namely CHAID and CART, are applied on empirical software cost data recorded in the ISBSG repository. Approximately 1000 project data records are selected for analysis and experimentation, with fuzzy decision trees instances being generated and evaluated based on prediction accuracy. The set of association rules extracted is used for providing mean effort value ranges. The experimental results suggest that the proposed approach may provide accurate cost predictions in terms of effort. In addition, there is strong evidence that the fuzzy transformation of cost drivers contribute to enhancing the estimation process.

The software cost estimation process is one of the most critical managerial activities related to project planning, resource allocation and control. As software development is a highly dynamic procedure, the difficulty of providing accurate cost estimations tends to increase with development complexity. The inherent problems of the estimation process stem from its dependence on several complex variables, whose values are often imprecise, unknown, or incomplete, and their interrelationships are not easy to comprehend. Current software cost estimation models do not inspire enough confidence and accuracy with their predictions. This is mainly due to the models' sensitivity to project data values, and this problem is amplified because of the vast variances found in historical project attribute data. This paper aspires to provide a framework for evolving value ranges for cost attributes and attaining mean effort values using the Al-oriented problem-solving approach of genetic algorithms, with a twofold aim. Firstly, to provide effort estimations by analogy to the projects classified in the evolved ranges and secondly, to identify any present correlations between effort and cost attributes.

This paper proposes a dynamic software testing framework, which is able to analyse the source code of a program, create the necessary data structures for automatic testing, such as control flow graphs, and generate a near to optimum set of test cases with reference to a test coverage criterion. The framework consists of two sub-systems: the first is a program analysis system that identifies the type of statements and the complexity of conditions, performs analysis of variables, extracts code paths and creates the control flow graph (CFG) of the program under testing. The second is a test system that uses the CFG for automatically generating test data based on evolutionary computing. The latter system utilises a specially designed genetic algorithm to produce the set of test cases satisfying the selected coverage criterion. The efficacy and performance of the proposed testing approach is assessed and validated using a variety of sample programs.

Modern software development is currently seeking new paths to improve quality and meet time and cost constraints. Reuse of existing software components is considered one of these paths. However, this process experiences significant problems related to efficiently maintaining component repositories, and, moreover, providing the means to discover and retrieve the most suitable ones. This paper aims to provide a methodology to improve the component-based software development process. Specifically, its objective is to introduce an approach that reduces the time to locate suitable software components. The suggested methodology meets the requirements for the efficient searching of components in repositories and also addresses the need for adequate retrieval of the most suitable software components based on the needs of developers. To achieve this we employ a combination of partitional clustering algorithms borrowed from the field of computational intelligence and fuzzy logic thus creating a subset of the available components that are most suitable to the developers' preferences.

This work proposes a new methodology for intelligent classification and retrieval of software components based on user-defined requirements. The classification scheme utilizes a dedicated genetic algorithm which evolves a small number of classifiers by dividing the set of available components stored in a database into certain subsets (clusters). Each classifier thus becomes the leader-representative of its cluster. When a user wishes to trace a component he/she identifies the desired characteristics (component profile) which are then compared with the characteristics of the available classifiers. The closest classifier matching the required characteristics over a user-defined threshold will result in the "winning" set of components belonging to its cluster, which are presented to the user in descending matching fitness. We have validated our methodology over a synthetic dataset of components and the results obtained were very encouraging. Last, we present the Web application developed to support the proposed intelligent classification method

This paper proposes an extension of genetically evolved fuzzy cognitive maps (GEFCMs) used for decision-making, aiming at increasing their reliability and overcoming its main weakness which lies with the recalculation of weights corresponding to more than one concept every time a new multiple scenario is introduced. A new evolutionary approach is proposed to support multi-objective decision-making based on the introduction of a dedicated genetic algorithm (GA), which is responsible for finding an optimal weight matrix that satisfies two or more activation levels among the participating concept nodes. This evolutionary methodology is very appealing since it offers the optimal solution without a problem-solving strategy once the requirements are defined

The present paper proposes a methodology for engineering requirements in a precise, comprehensive, understandable and time-saving way based on concepts and principles of natural language syntax and semantics (NLSS). The NLSS methodology copes with the following phases: (i) requirements discovery, by emphasizing on the creation and application of specific questions sets; (ii) requirements analysis, by stressing issues such as classification, decomposition and terminology of requirements; and (iii) requirements specification, by focusing on the writing formalization of the requirements.

A systematic investigation of the effect of different neural network architecture alternatives for predicting the future course of stock prices in the Cyprus Stock Exchange (CSE) market is conducted. This market exhibited an abrupt increase in the general index price during the last year, thus forming a very interesting case for research purposes. The influence of various economic and political factors, from both the local and the international scene, have also been examined. The main conclusion drawn is that the CSE market is governed by unique conditions which when properly modeled can yield successful predictions.