• A dynamic trend analysis framework for Stack Exchange communities is introduced. • Two indicators for measuring topic growth are introduced. • A classifier to identify ChatGPT-related questions is introduced. • Tag clustering combining Inclusion Index with Affinity Propagation is used. • An overhauled visualization tool from our previous work is presented. Technological and methodological trends emerge at unprecedented rates, attracting developers to explore their potential and seek advice in online social networks. In this spectrum, ChatGPT has become a popular technology used for generating content to satisfy user queries while developers also integrate its mechanisms into their applications. Social networks usually revolve around technological trends through relevant announcements, posts, and questions. The primary goal is to demystify and evaluate the content surrounding questions from developers on Stack Overflow (SO) regarding a trending technology or method, in this case, ChatGPT. We present a topic-oriented trend analysis framework for analyzing questions from Stack Exchange communities, formulating a case study with five Research Questions (RQs) adapted to ChatGPT-related queries posted on Stack Overflow. The proposed framework contains different components aimed at extracting the main topics of relevant questions, providing analytics and pipelines for evaluating and comparing topic popularity, difficulty, and trending ability, as well as filtering irrelevant questions. The analysis uncovers diverse topics referring to technologies, platforms, and programming languages associated with ChatGPT usage, as well as a variety of purposes related to textual, audio, and image data. Additionally, the framework helped in identifying one popular and one unpopular topic, along with one difficult and four rising topics. In the context of ChatGPT, statistical tests indicated that Langchain is a more popular framework than Flutter and that questions related to the ChatGPT API concentrate lower scores but more answers than questions associated with LLMs. Overall, this paper demonstrates that the introduced framework can be utilized for studying multiple objectives covering a trending subject, as its mechanisms rely exclusively on the standard characteristics of Stack Exchange (SE) questions. Also, the methodologies and findings can offer insights and ideas for future research and experiments.

Context Possessing up-to-date knowledge, skills and transversal competencies (KSTs) is essential for both the successful delivery of software projects and a career in software engineering (SE). However, the technological landscape is changing rapidly, posing continuous challenges: for professionals entering the market or pivoting careers, for organizations hiring and monitoring workforce expertise and for educational institutes designing or updating their curricula. Objectives We study job requirements within and across SE occupations (Applications Programmers, Software Developers, Systems Analysts, Web and Multimedia Developers) to assist software organizations to better face skill mismatch and skills’ gap problems, software engineers in upskilling and reskilling endeavors and software education institutes in providing more industrially relevant curricula. Method In this study, we leverage a large corpus of online job advertisements, which are jointly collected by CEDEFOP and Eurostat. The dataset is analyzed through the lens of concepts and techniques from the study of biodiversity of species to assess the variation of expertise and identify skills that are transferable or unique in these occupations. Specifically, we adopt established diversity indices, such as alpha diversity, beta diversity, ordination methods, and indicator species analysis, aiming to quantify both the variety of skills within occupations and the differences across them. This approach highlights both the breadth and distinctiveness of expertise across occupations, rendering the biodiversity perspective a central and practical part of our methodology. Results The results reveal that the complete list of KSTs that is used to characterize the profiles of OJAs for SE-related occupations is very broad and that skillset required for each occupation is quite distinct, since there are statistically significant differences in the composition of the skillsets. Transversal Skills and Competences (T) appear to be the most transferable qualification; or “adapt to change” and “work in teams” are the KSTs that appears more uniformly to all studied software occupations, and “computer programming” is the top hard-skill that appears more uniformly to all occupations. However, each occupation shows some specific qualifications. Conclusion The results are contrasted against the literature, are interpreted, various implications to researchers and practitioners are provided, and a retrospective analysis of the tailoring of the biodiversity approach to SE labor landscape is provided. Overall, the proposed biodiversity analysis adds value by providing a novel, theory-driven methodology to assess skill variation, identifying both common and occupation-specific KSTs, and supporting evidence-based workforce and curriculum design.

Scientific Software Development (SSD) plays a pivotal role in accelerating research and solving real-world problems across disciplines. Yet, the field remains fragmented, lacking a unified framework for analyzing the skills embedded in open-source scientific software. To address this gap, we introduce SciESCO , a tri-phasic framework that combines bibliometric analysis, skill extraction and predictive modeling to provide actionable insights into evolving competencies. In the first phase, focusing on 2239 publications from 2015 to 2025 in SoftwareX and Software Impacts, we analyze scientific software contributions, as these are the only peer-reviewed journals that publish software tools accompanied by complete and openly available metadata, including source code and related documentation. In the second phase, skills are extracted using ESCOX, an open-source tool developed under the SKILLAB EU project that leverages the European Skills/Competences, Qualifications, and Occupations (ESCO) taxonomy. These extracted skills represent the supply side of the labour market and are subsequently classified into eight scientific domains using a large language model (LLM). Finally, a Graph Neural Network (GNN) predicts links between skills and domains. Our results highlight critical competencies such as machine learning, authoring software, UI design, deep learning and data visualization. The SciESCO framework offers a skill-oriented view of SSD, supporting HR professionals, researchers and institutions in aligning talent with scientific needs. This study also identifies gaps in mapping competencies to domains, opening avenues for future research in skill analytics and offering a forward-looking view of the future SSD researcher. Finally, SciESCO provides an open-source tool designed for implementation by stakeholders, practitioners and researchers.

As society is continuously adapting to technological change and progress, fast-moving digital transformations are the driving force for setting the necessary skillsets for the workforce. Furthermore, the advent of Industry 5.0 as a defining concept for the future, which advocates a human-centric coalescence of humans and technology or software, renders the skilled workforce the most important asset in any organization or business. The endgame of the digital transformation is to evoke the reshaping, evolution, or replacement of traditional and possibly obsolete processes at intra- or inter-organizational levels in multiple aspects, introducing innovative ways of re-defining the workforce. In this context SKILLAB will act as a smart tool for handling, honing, and widening the competencies of the personnel of companies, forecasting future skill gaps and providing European citizens with a tool for upskilling and reskilling.

With medical technological advances being developed in a rapid pace, the need for effective Scientific Software Development (SSD), that can process, store and visualize medical data is ever growing. Particularly during the COVID-19 pandemic, the medical community came together to produce efficient solutions to tackle this global setback. Programmers and developers have an active role in the procurement of medical software, with many of them exchanging knowledge and opinions in Q&A portals like Stack Overflow (SO) about methodologies, techniques and programming queries. In this study we present a data-driven framework that collects, filters, stores and analyzes issues and questions for medical applications from SO, visualizing them in an intuitive manner. To highlight the functionalities of our framework, we present a case study with COVID-19 SSD related questions, providing insights and valuable information about the status of the domain.

Technical Debt (TD) is a successful metaphor in conveying the consequences of software inefficiencies and their elimination to both technical and non-technical stakeholders, primarily due to its monetary nature. The identification and quantification of TD rely heavily on the use of a small handful of sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue we use 18 metrics pertaining to source code, repository activity, issue tracking, refactorings, duplication and commenting rates of each class as features for statistical and Machine Learning models, so as to classify them as High-TD or not. As a benchmark we exploit 18.857 classes obtained from 25 Java projects, whose high levels of TD has been confirmed by three leading tools. The findings indicate that it is feasible to identify TD issues with sufficient accuracy and reasonable effort: a subset of superior classifiers achieved an F2-measure score of approximately 0.79 with an associated Module Inspection ratio of approximately 0.10. Based on the results a tool prototype for automatically assessing the TD of Java projects has been implemented.

As the industry is moving towards digitalized solutions and practices, a growth in remote working has been observed with companies embracing flexibility for their workforce. Global crises, such as the coronavirus pandemic, have also accelerated this process, transforming the labor market. This trend is reflected in job portals, that contain an increasing number of remote job advertisements. Recognizing this evolving change, we perform a thorough study in Stack Overflow, to examine the main characteristics of remote working that discriminate it from its on-site counterpart. By collecting and analyzing 8514 job posts and leveraging text mining and graph theory methodologies, we attempt to pinpoint the primary elements that define each category, from dominant technologies to job positions and top seeking industries. The findings suggest that remote working presents differences from traditional working, being mainly associated with the software engineering sector and with well-known software development and data analytics technologies.

The Covid-19 outbreak has changed to an unprecedented extent almost every aspect of human activity. At the same time, the pandemic has stimulated enormous amount of research by scientists across various disciplines, seeking to study the phenomenon itself, its epidemiological characteristics and ways to confront its consequences. Information Technology, and particularly Data Science, drive innovation in all related to Covid-19 biomedical fields. Acknowledging that software developers routinely resort to open `question & answer' communities like Stack Overflow to seek advice on solving technical issues, we have performed an empirical study to investigate the extent, evolution and characteristics of Covid-19 related posts. Through the study of 464 Stack Overflow questions posted in February and March 2020 and leveraging the power of text mining, we attempt to shed light into the interest of developers in Covid-19 related topics and the most popular problems for which the users seek information. The findings reveal that indeed this global crisis sparked off an intense activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid- 19 data, primarily using Python technologies.

Free and open source software has gained a lot of momentum in the industry and the research community. Open source licenses determine the rules, under which the open source software can be further used and distributed. Previous works have examined the usage of open source licenses in the framework of specific projects or online social coding platforms, examining developers specific licensing views for specific software. However, the questions practitioners ask about licenses and licensing as captured in Question and Answer websites also constitute an important aspect toward understanding practitioners general licenses and licensing concerns. In this paper, we investigate open source license discussions using data from the Software Engineering, Open Source and Law Stack Exchange sites that contain relevant data. We describe the process used for the data collection and analysis, and discuss the main results that can be useful for developers, educators and license authors. Our results indicate that clarifications about specific licenses and specific license terms are required.

The cornerstones of technical debt (TD) are two concepts borrowed from economics: principal and interest. Although in economics the two terms are related, in TD there is no study on this direction so as to validate the strength of the metaphor. We study the relation between Principal and Interest, and subsequently dig further into the ‘ingredients’ of each concept (since they are multi-faceted). In particular, we investigate if artifacts with similar levels of TD Principal exhibit a similar amount of TD Interest, and vice-versa. To achieve this goal, we performed an empirical study, analyzing the dataset using the Mantel test. Through the Mantel test, we examined the relation between TD Principal and Interest, and identified aspects that are able to denote proximity of artifacts, with respect to TD. Next, through Linear Mixed Effects (LME) modelling we studied the generalizability of the results. The results of the study suggest that TD Principal and Interest are related, in the sense that classes with similar levels of TD Principal tend to have similar levels of Interest. Additionally, we have reached the conclusion that aggregated measures of TD Principal or Interest are more capable of identifying proximate artifacts, compared to isolated metrics. Finally, we have provided empirical evidence on the fact that improving certain quality properties (e.g., size and coupling) should be prioritized while ranking refactoring opportunities in the sense that high values of these properties are in most of the cases related to artifacts with higher levels of TD Principal. The findings shed light on the relations between the two concepts, and can be useful for both researchers and practitioners: the former can get a deeper understanding of the concepts, whereas the latter can use our findings to guide their TD management processes such as prioritization and repayment.

Abstract Context The rapid evolvement of software development effort estimation models created the need for empirical evaluation of their quality. The empirical evaluation is based either on hypothesis tests with respect to a single criterion or on aggregating methods for multiple criteria. However, a model can be considered as a multidimensional entity performing differently on alternative datasets and its performance can be divergent when expressed by alternative criteria. Objective In this study, we explore this multidimensional nature of models by considering them as points in two different spaces (domain and criteria spaces). Method Introducing an alternative approach for data‐driven benchmarking, a new framework based on archetypal analysis is proposed for evaluation purposes of multiple models. Results The benefits of the framework are illustrated through a large‐scale experimental setup on a set of 93 effort estimation models, trained and tested on 10 datasets under 8 criteria providing answers to critical research questions. Conclusion The results indicate that a small minority of reference models is enough to define the performance of the bulk of all models. The framework focuses on models that have behavior close to archetypes and especially those that are close to a “best” archetype.

We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development.We compare output to other countries of similar geography, economy, and size-based on publication counts recorded in the literature-and predict future growth based on those counts as well as national priority areas.Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries.Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.

The set of concepts collectively known as Technical Debt (TD) assume that software liabilities set up a context that can make a future change more costly or impossible; and therefore repaying the debt should be pursued. However, software developers often disagree with an automatically generated list of improvement suggestions, which they consider not fitting or important for their own code. To shed light into the reasons that drive developers to adopt or reject refactoring opportunities (i.e. TD repayment), we have performed an empirical study on the potential factors that affect the developers' decision to agree with the removal of a specific TD liability. The study has been addressed to the developers of four well-known open-source applications. To increase the response rate, a personalized assessment has first been sent to each developer, summarizing his/her own contribution to the TD of the corresponding project. Responds have been collected through a custom built web application that presented code fragments suffering from violations as identified by SonarQube along with information that could possibly affect their level of agreement to the importance of resolving an issue. These factors include data such as the frequency of past changes in the module under study, the number of bugs, the type and intensity of the violation, the level of involvement of the developer and whether he/she is a contributor in the corresponding project. Multivariate statistical analysis methods have been used to understand the importance and the underlying relationships among these factors and the results are expected to be useful for researchers and practitioners in TD Management.

One of the first steps towards the effective Technical Debt (TD) management is the quantification and continuous monitoring of the TD principal. In the current state-ofresearch and practice the most common ways to assess TD principal are the use of: (a) structural proxies—i.e., most commonly through quality metrics; and (b) monetized proxies—i.e., most commonly through the use of the SQALE (Software Quality Assessment based on Lifecycle Expectations) method. Although both approaches have merit, they seem to rely on different viewpoints of TD and their levels of agreement have not been evaluated so far. Therefore, in this paper, we empirically explore this relation by analyzing data obtained from 20 open source software projects and build a regression model that establishes a relationship between them. The results of the study suggest that a model of seven structural metrics, quantifying different aspects of quality (i.e., coupling, cohesion, complexity, size, and inheritance) can accurately estimate TD principal as appraised by SonarQube. The results of this case study are useful to both academia and industry. In particular, academia can gain knowledge on: (a) the reliability and agreement of TD principal assessment methods and (b) the structural characteristics of software that contribute to the accumulation of TD, whereas practitioners are provided with an alternative evaluation model with reduced number of parameters that can accurately assess TD, through traditional software quality metrics and tools.

Skills and competences of people participating in online professional networks constitute an ever-increasing new source for data collection and analysis. An important sub-domain of human resources management (HRM) is the recruitment process. Job advertisements and people profiles are main parts of recruitment and since are now available online, they constitute a key factor of a new e-recruitment era. Data mining for erecruitment analysis is important in order to extract a knowledge base for people analytics. Skills and competences are the key variables for people analytics and can be drawn from job advertisements. Leveraging the raw information of online job offers, provides a rich source for people analytics. Detecting the appropriate skills and competences for a job from raw text data and associate them with a job seeker is an increasing challenge. The main objective of this paper is the proposal of a framework aiming to collect online job advertisements from a web source which concerns IT job offers and to extract from the raw text the required skills and competences for specific jobs. The selected professional networking web source is StackOverflow and multivariate statistical data analysis was used to test the correlations between skills and competences in the job offers dataset. The present work falls in a relatively new field of research, concerning the competence mining of peopleware data with special focus on software development.

The importance of providing accurate estimations of software cost in management life cycle has led to an overabundant pool of prediction candidates exhibiting certain advantages and limitations. Thus, there is an imperative need for well-established principles that will aid the right decision-making regarding the selection of the best candidate. Unfortunately, the choice of the most appropriate estimation technique is not a trivial task, due to the multi-faceted nature of error. Accuracy, bias and variance are notions describing different aspects of predictive power that someone has to take into consideration during the validation process. The main objective of this paper is the utilization of visual analytics for the evaluation of two fundamental ingredients of prediction accuracy: the bias and the variance. Through a bootstrap-based resampling algorithm, we provide an easy-to-interpret way in order to acquire significant knowledge about the quality of a prediction candidate and manage the uncertainty of the estimation process. Ensemble techniques utilizing the advantages of both simple and complex solo methods are possible balancing solutions to the problem of the bias-variance tradeoff.

Background: Despite the number of Web effort estimation techniques investigated, there is no consensus as to which technique produces the most accurate estimates, an issue shared by effort estimation in the general software estimation domain. A previous study in this domain has shown that using ensembles of estimation techniques can be used to address this issue. Aim: The aim of this paper is to investigate whether ensembles of effort estimation techniques will be similarly successful when used on Web project data. Method: The previous study built ensembles using solo effort estimation techniques that were deemed superior. In order to identify these superior techniques two approaches were investigated: The first involved replicating the methodology used in the previous study, while the second approach used the Scott-Knott algorithm. Both approaches were done using the same 90 solo estimation techniques on Web project data from the Tukutuku dataset. The replication identified 16 solo techniques that were deemed superior and were used to build 15 ensembles, while the Scott-Knott algorithm identified 19 superior solo techniques that were used to build two ensembles. Results: The ensembles produced by both approaches performed very well against solo effort estimation techniques. With the replication, the top 12 techniques were all ensembles, with the remaining 3 ensembles falling within the top 17 techniques. These 15 effort estimation ensembles, along with the 2 built by the second approach, were grouped into the best cluster of effort estimation techniques by the Scott-Knott algorithm. Conclusion: While it may not be possible to identify a single best technique, the results suggest that ensembles of estimation techniques consistently perform well even when using Web project data.

Managers of enterprises concern with a major challenge for optimal management of human resources based on availability of domain experts and highly qualified personnel. The process of allocating right people to the right positions in a right time is a key to success. To achieve this goal, managers need to deploy evaluation tools integrated with the gap analysis method. This paper presents the concept and implementation details of an in-house developed software tool for competence evaluation of domain specific competencies and selection of professionals. A generic mathematical representation of competences in this project makes the software tool applied in a wide variety of organizations. A standard competence model has been first defined in this project with 5 main competence categories and related sub-categories including over 70 competence questionnaires in different managerial and employee levels. Test and evaluation of the software have been carried out by initializing the lab data of over 50 candidates with student groups involved in the project at the institute of Knowledge Based Systems and Knowledge Management, University of Siegen. The paper reflects the conception and the outcomes of the implementation of the software tool. The ultimate objective of this interdisciplinary project is to fill the gap in the selection process by means of an efficient and practical competency evaluation tool. The generic software tool is aimed to be used as a component in research and industrial projects of the institute.

Software Cost Estimation can be described as the process of predicting the most realistic effort required to complete a software project. Due to the strong relationship of accurate effort estimations with many crucial project management activities, the research community has been focused on the development and application of a vast variety of methods and models trying to improve the estimation procedure. From the diversity of methods emerged the need for comparisons to determine the best model. However, the inconsistent results brought to light significant doubts and uncertainty about the appropriateness of the comparison process in experimental studies. Overall, there exist several potential sources of bias that have to be considered in order to reinforce the confidence of experiments. In this paper, we propose a statistical framework based on a multiple comparisons algorithm in order to rank several cost estimation models, identifying those which have significant differences in accuracy, and clustering them in nonoverlapping groups. The proposed framework is applied in a large-scale setup of comparing 11 prediction models over six datasets. The results illustrate the benefits and the significant information obtained through the systematic comparison of alternative methods.

Software quality is one of the main goals of effective programming. Although it has a quite ambiguous meaning, quality can be measured by several metrics, which have been appropriately formulated through the years. Software measurement is a particularly important procedure, as it provides meaningful information about the software artifact. This procedure is even more emerging when we refer to open source software, where the need for shared knowledge is crucial for the maintenance and evolution of the code. A paradigm of open source project where code quality is especially important is the scientific language R. This paper aims to perform measurements on the R statistical open source software, examine the relationships among the observed metrics and special attributes of the R software and search for certain characteristics that define its behavior and structure. For this purpose, a random sample of 508 R packages has been downloaded from the CRAN repository of R and has been measured, using the SourceMonitor metrics tool. The resulted measurements, along with a significant number of specific attributes of the R packages, were examined and analyzed, leading to interesting conclusions such as the validity of a power law distribution regarding the majority of the sample's metrics and the absence of specific patterns due to the interdependencies among packages. Finally, the effects of the number of developers and the number of dependencies are investigated, in order to understand their impact on the metrics of the sample packages.

Background: Software quality issues are commonly reported when off shoring software development. Value-based software engineering addresses this by ensuring key stakeholders have a common understanding of quality. Aim: This work seeks to understand the levels of alignment between key stakeholders on aspects of software quality for two products developed as part of an offshore in sourcing arrangement. The study further aims to explain the levels of alignment identified. Method: Representatives of key stakeholder groups for both products ranked aspects of software quality. The results were discussed with the groups to gain a deeper understanding. Results: Low levels of alignment were found between the groups studied. This is associated with insufficiently defined quality requirements, a culture that does not question management and conflicting temporal reflections on the product's quality. Conclusion: The work emphasizes the need for greater support to align success-critical stakeholder groups in their understanding of quality when off shoring software development.

We present the first results from a survey carried out over the second quarter of 2009 examining how theories in object-oriented design are understood and used by software developers. We collected 3785 responses from software developers world-wide, which we believe is the largest survey of its kind. We targeted the use of encapsulation, class size as measured by number of methods, and depth of a class in the inheritance hierarchy. We found that, while overall practitioners followed advice on encapsulation, there was some variation of adherence to it. For class size and depth there was substantially less agreement with expert advice. In addition, inconsistencies were found within the use and perception of object-oriented concepts within the investigated group of developers. The results of this survey has deep reaching consequences for both practitioners and researchers as they highlight and confirm central issues.

Software Cost Estimation is the task of predicting the effort or productivity required to complete a software project. Two of the most known techniques appeared in literature so far are Regression Analysis and Estimation by Analogy. The results of the empirical studies show the lack of convergence in choosing the best prediction technique between the parametric Regression Analysis and the non-parametric Estimation by Analogy models. In this paper, we introduce the use of a semi-parametric model that achieves to incorporate some parametric information into a non-parametric model combining in this way regression and analogy. Furthermore, we demonstrate the procedure of building such a model on two well-known datasets and we present the comparative results based on the predictive accuracy of the new technique using several accuracy measures. We also perform statistical tests on the residuals in order to assess the improvement in the predictions attained through the new semi-parametric model in comparison to the accuracy of Regression Analysis and Estimation by Analogy when applied separately. Our results show that the semi-parametric model provides more accurate predictions than each one of the parametric and non-parametric approaches.

MOTIVATION: Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed. METHODS: PuReD-MCL avoids using natural language processing (NLP) techniques directly; instead, it takes advantage of existing resources, available from PubMed. PuReD-MCL then clusters documents efficiently using the MCL graph clustering algorithm, which is based on graph flow simulation. This process allows users to analyse the results by highlighting important clues, and finally to visualize the clusters and all relevant information using an interactive graph layout algorithm, for instance BioLayout Express 3D. RESULTS: The methodology was applied to two different datasets, previously used for the validation of the document clustering tool TextQuest. The first dataset involves the organisms Escherichia coli and yeast, whereas the second is related to Drosophila development. PuReD-MCL successfully reproduces the annotated results obtained from TextQuest, while at the same time provides additional insights into the clusters and the corresponding documents. AVAILABILITY: Source code in perl and R are available from http://tartara.csd.auth.gr/~theodos/

Change impact analysis is a change management activity that previously has been studied much from a technical perspective. For example, much work focuses on methods for determining the impact of a change. In this paper, we present results from a study on the role of impact analysis in the change management process. In the study, impact analysis issues were prioritised with respect to criticality by software professionals from an organisational perspective and a self-perspective. The software professionals belonged to three organisational levels: operative, tactical and strategic. Qualitative and statistical analyses with respect to differences between perspectives as well as levels are presented. The results show that important issues for a particular level are tightly related to how the level is defined. Similarly, issues important from an organisational perspective are more holistic than those important from a self-perspective. However, our data indicate that the self-perspective colours the organisational perspective, meaning that personal opinions and attitudes cannot easily be disregarded. In comparing the perspectives and the levels, we visualise the differences in a way that allow us to discuss two classes of issues: high-priority and medium-priority. The most important issues from this point of view concern fundamental aspects of impact analysis and its execution.

Abstract Checkpointing has a crucial impact on systems' performance and fault‐tolerance effectiveness: excessive checkpointing results in performance degradation, while deficient checkpointing incurs expensive recovery. In distributed systems with independent checkpoint activities there is no easy way to determine checkpoint frequencies optimizing response‐time and fault‐tolerance costs at the same time. The purpose of this paper is to investigate the potentialities of a statistical decision‐making procedure. We adopt a simulation‐based approach for obtaining performance metrics that are afterwards used for determining a trade‐off between checkpoint interval reductions and efficiency in performance. Statistical methodology including experimental design, regression analysis and optimization provides us with the framework for comparing configurations, which use possibly different fault‐tolerance mechanisms (replication‐based or message‐logging‐based). Systematic research also allows us to take into account additional design factors, such as load balancing. The method is described in terms of a standardized object replication model (OMG FT‐CORBA), but it could also be applied in other (e.g. process‐based) computational models. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents the application and evaluation by means of a controlled experiment of an instructional tool during an open and distance learning (ODL) course. The core issue of investigation is whether this instructional aid can support, guide and scaffold the distant student in his/her study. For this purpose, a controlled experiment was conducted with the participation of 191 undergraduate students. Descriptive statistics as well as a variety of statistical methods have been applied to the collected data, in order to test the research hypotheses. The results have shown a statistical significant difference in performance for the student group that used the tool. Finally, concerns about the application of the tool in a broader context and further research on the area are also presented

We describe a controlled experiment concerning the use of a learning aid during the instructional procedure. The core issue of investigation is whether this instructional aid can augment the cognitive transfer of the learners by personalizing the offered knowledge. A controlled experiment was performed with the participation of 79 students. The results have shown that for the transfer of simple information this "lesson sheet" does not provide any statistically significant advantage, yet for complex information a statistically significant better performance is observed for the student group that used the tool.