Context: The usability software quality characteristic aims to improve system user performance. In a previous study, we found evidence of the impact of a set of usability features from the viewpoint of users in terms of efficiency, effectiveness and satisfaction. However, the impact level appears to depend on the usability feature and suggest priorities with respect to their implementation depending on how they promote user performance.Objectives: We use a family of three experiments to increase the precision and generalization of the results in the baseline experiment and provide findings regarding the impact on user performance of the Abort Operation, Progress Feedback and Preferences usability mechanisms.Method: We conduct two replications of the baseline experiment in academic settings. We analyse the data of 366 experimental subjects and apply aggregation (meta-analysis) procedures.Results: We find that the Abort Operation and Preferences usability mechanisms appear to improve system usability a great deal with respect to efficiency, effectiveness and user satisfaction.Conclusions: We find that the family of experiments further corroborates the results of the baseline experiment. Most of the results are statistically significant, and, because of the large number of experimental subjects, the evidence that we gathered in the replications is sufficient to outweigh other experiments.

Context:Recent developments in natural language processing have facilitated the adoption of chatbots in typically collaborative software engineering tasks (such as diagram modelling). Families of experiments can assess the performance of tools and processes and, at the same time, alleviate some of the typical shortcomings of individual experiments (e.g., inaccurate and potentially biased results due to a small number of participants).Objective:Compare the usability of a chatbot for collaborative modelling (i.e., SOCIO) and an online web tool (i.e., Creately).Method:We conducted a family of three experiments to evaluate the usability of SOCIO against the Creately online collaborative tool in academic settings.Results:The student participants were faster at building class diagrams using the chatbot than with the online collaborative tool and more satisfied with SOCIO. Besides, the class diagrams built using the chatbot tended to be more concise —albeit slightly less complete.Conclusion:Chatbots appear to be helpful for building class diagrams. In fact, our study has helped us to shed light on the future direction for experimentation in this field and lays the groundwork for researching the applicability of chatbots in diagramming.

Context: Researchers from different groups and institutions are collaborating on building groups of experiments by means of replication (i.e., conducting groups of replications). Disparate aggregation techniques are being applied to analyze groups of replications. The application of unsuitable techniques to aggregate replication results may undermine the potential of groups of replications to provide in-depth insights from experiment results. Objectives: Provide an analysis procedure with a set of embedded guidelines to aggregate software engineering (SE) replication results. Method: We compare the characteristics of groups of replications for SE and other mature experimental disciplines such as medicine and pharmacology. In view of their differences, the limitations with regard to the joint data analysis of groups of SE replications and the guidelines provided in mature experimental disciplines to analyze groups of replications, we build an analysis procedure with a set of embedded guidelines specifically tailored to the analysis of groups of SE replications. We apply the proposed analysis procedure to a representative group of SE replications to illustrate its use. Results: All the information contained within the raw data should be leveraged during the aggregation of replication results. The analysis procedure that we propose encourages the use of stratified individual participant data and aggregated data in tandem to analyze groups of SE replications. Conclusion: The aggregation techniques used to analyze groups of replications should be justified in research articles. This will increase the reliability and transparency of joint results. The proposed guidelines should ease this endeavor.

Modelling is a fundamental activity in software engineering, which is often performed in collaboration. For this purpose, on-line tools running on the cloud are frequently used. However, recent advances in Natural Language Processing have fostered the emergence of chatbots, which are increasingly used for all sorts of software engineering tasks, including modelling. To evaluate to what extent chatbots are suitable for collaborative modelling, we conducted an experimental study with 54 participants, to evaluate the usability of a modelling chatbot called SOCIO, comparing it with the on-line tool Creately. We employed a within-subjects cross-over design of 2 sequences and 2 periods. Usability was determined by attributes of efficiency, effectiveness, satisfaction and quality of the results. We found that SOCIO saved time and reduced communication effort over Creately. SOCIO satisfied users to a greater extent than Creately, while in effectiveness results were similar. With respect to diagram quality, SOCIO outperformed Creately in terms of precision, while solutions with Creately had better recall and perceived success. However, in terms of accuracy and error scores, both tools were similar.

Abstract Software engineering (SE) experiments suffer from threats to validity that may impact their results. Replication allows researchers building on top of previous experiments’ weaknesses and increasing the reliability of the findings. Illustrating the benefits of replication to increase the reliability of the findings and uncover moderator variables. We replicate an experiment on test-driven development (TDD) and address some of its threats to validity and those of a previous replication. We compare the replications’ results and hypothesize on plausible moderators impacting results. Differences across TDD replications’ results might be due to the operationalization of the response variables, the allocation of subjects to treatments, the allowance to work outside the laboratory, the provision of stubs, or the task. Replications allow examining the robustness of the findings, hypothesizing on plausible moderators influencing results, and strengthening the evidence obtained.

Context: Families of experiments (i.e., groups of experiments with the same goal) are on the rise in Software Engineering (SE). Selecting unsuitable aggregation techniques to analyze families may undermine their potential to provide in-depth insights from experiments' results. Objectives: Identifying the techniques used to aggregate experiments' results within families in SE. Raising awareness of the importance of applying suitable aggregation techniques to reach reliable conclusions within families. Method: We conduct a systematic mapping study (SMS) to identify the aggregation techniques used to analyze families of experiments in SE. We outline the advantages and disadvantages of each aggregation technique according to mature experimental disciplines such as medicine and pharmacology. We provide preliminary recommendations to analyze and report families of experiments in view of families' common limitations with regard to joint data analysis. Results: Several aggregation techniques have been used to analyze SE families of experiments, including Narrative synthesis, Aggregated Data (AD), Individual Participant Data (IPD) mega-trial or stratified, and Aggregation of p-values. The rationale used to select aggregation techniques is rarely discussed within families. Families of experiments are commonly analyzed with unsuitable aggregation techniques according to the literature of mature experimental disciplines. Conclusion: Data analysis' reporting practices should be improved to increase the reliability and transparency of joint results. AD and IPD stratified appear to be suitable to analyze SE families of experiments.

Test-Driven Development (TDD), an agile development approach that enforces the construction of software systems by means of successive micro-iterative testing coding cycles, has been widely claimed to increase external software quality. In view of this, some managers at Paf-a Nordic gaming entertainment company-were interested in knowing how would TDD perform at their premises. Eventually, if TDD outperformed their traditional way of coding (i.e., YW, short for Your Way), it would be possible to switch to TDD considering the empirical evidence achieved at the company level. We conduct an experiment at Paf to evaluate the performance of TDD, YW and the reverse approach of TDD (i.e., ITL, short for Iterative-Test Last) on external quality. TDD outperforms YW and ITL at Paf. Despite the encouraging results, we cannot recommend Paf to immediately adopt TDD as the difference in performance between YW and TDD is small. However, as TDD looks promising at Paf, we suggest to move some developers to TDD and to run a future experiment to compare the performance of TDD and YW. TDD slightly outperforms ITL in controlled experiments for TDD novices. However, more industrial experiments are still needed to evaluate the performance of TDD in real-life contexts.

Context: Researchers from different groups and institutions are collaborating towards the construction of groups of interrelated replications. Applying unsuitable techniques to aggregate interrelated replications' results may impact the reliability of joint conclusions.

In large organizations and small firms in transportation, there is a growing need to use and analyze spatial data. Transportation system analysis and planning as well as mobility studies frequently use Geographic Information Systems (GIS). In this paper we propose the development of a web services platform dedicated to transportation and logistics. Taking advantage of the web services development framework PyOpenCF we integrate in the same environment services oriented to geolocalization, logistical optimization, etc. We develop a PyOpenCF client whose graphic interface allows processes with spatial data to be launched and provides a visualization of the results.

The adoption of Web Service standards provides us with an increased level of manageability, extensibility and interoperability between loosely coupled services.The adoption of Web Services technologies atop sites for performance monitoring and scheduling will improve the efficient use of the computational resources. Web Services provide the ability to decompose HPC resources and functionality into a set of discoverable and loosely coupled services, which are capable of interaction in heterogeneous environments. At the same time, Web Services can address many of the interoperability issues that can be encountered in large scale systems.End users can access to these services to decide which system will be the most suitable for their needs. Other tools like schedulers can use the resources available as services to optimize the HPC resources and minimize jobs waiting time.

Web services-based technologies have emerged as a technological alternative for computational web portals. Facilitating access to distributed resources through web interfaces while simultaneously ensuring security is one of the main goals in most of the currently existing manifold tools and frameworks. OpenCF, the open source computational framework that we have developed, shares these objectives and adds others, like enforced portability, genericity, modularity and compatibility with a wide range of high performance computing systems.

We present our joint effort to develop a Web based interface for the GNU Scientific library and its parallelization. The interface has been developed using standard Web services technology to enable the use of non local resources to execute parallel programs. The final result is a computing service where sequential and parallel routines demanding high performance computing are supplied. The design allows to incorporate new servers and platforms with a small number of software requirements.