159,088 research outputs found

    Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

    Full text link
    This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage

    Policy Implications of a Behavioural Economics Analysis of Land Use Determinants in Rural Scotland

    Get PDF
    The paper analyses the land use behaviour of Scottish land managers and the factors influencing it in the current context of the EU rural land use policies. The analysis employs a frequently used behavioural economics method, namely structural equation modelling (SEM). Central to the empirical analysis in this paper is a cross-section database containing data collected in May to June 2009 through telephone interviews of 600 land managers in Scotland. The model tests and estimates the relationships between land use behaviour, i.e., behavioural intentions to change the size of business/holding, and several of its a priori determinants found significant in the scientific literature. The results indicate that a stronger propensity to change size of their businesses is exhibited by younger land managers who intend to pass their land on to family, with larger land size and stronger attitudes towards increasing it, with lower percentage of their income made up from Government support, who are less likely to have perceived changes in regulation and input/output prices as having an impact on their business, who discuss and plan changes in size of business with their banks/building societies, and frequently access sources of information to help with their strategic decisions.Land use, rural policies, Scotland, structural equation modelling, Land Economics/Use,

    Investigating Automatic Static Analysis Results to Identify Quality Problems: an Inductive Study

    Get PDF
    Background: Automatic static analysis (ASA) tools examine source code to discover "issues", i.e. code patterns that are symptoms of bad programming practices and that can lead to defective behavior. Studies in the literature have shown that these tools find defects earlier than other verification activities, but they produce a substantial number of false positive warnings. For this reason, an alternative approach is to use the set of ASA issues to identify defect prone files and components rather than focusing on the individual issues. Aim: We conducted an exploratory study to investigate whether ASA issues can be used as early indicators of faulty files and components and, for the first time, whether they point to a decay of specific software quality attributes, such as maintainability or functionality. Our aim is to understand the critical parameters and feasibility of such an approach to feed into future research on more specific quality and defect prediction models. Method: We analyzed an industrial C# web application using the Resharper ASA tool and explored if significant correlations exist in such a data set. Results: We found promising results when predicting defect-prone files. A set of specific Resharper categories are better indicators of faulty files than common software metrics or the collection of issues of all issue categories, and these categories correlate to different software quality attributes. Conclusions: Our advice for future research is to perform analysis on file rather component level and to evaluate the generalizability of categories. We also recommend using larger datasets as we learned that data sparseness can lead to challenges in the proposed analysis proces

    Software evolution prediction using seasonal time analysis: a comparative study

    Get PDF
    Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data

    Use of habitat suitability modeling in the integrated urban water system modeling of the Drava River (Varazdin, Croatia)

    Get PDF
    The development of practical tools for providing accurate ecological assessment of rivers and species conditions is necessary to preserve habitats and species, stop degradation and restore water quality. An understanding of the causal mechanisms and processes that affect the ecological water quality and shape macroinvertebrate communities at a local scale has important implications for conservation management and river restoration. This study used the integration of wastewater treatment, river water quality and ecological assessment models to study the effect of upgrading a wastewater treatment plant (WWTP) and their ecological effects for the receiving river. The WWTP and the water quality and quantity of the Drava river in Croatia were modelled in the software WEST. For the ecological modeling, the approach followed was to build habitat suitability and ecological assessment models based on classification trees. This technique allows predicting the biological water quality in terms of the occurrence of macroinvertebrates and the river status according to ecological water quality indices. The ecological models developed were satisfactory, and showed a good predictive performance and good discrimination capacity. Using the integrated ecological model for the Drava river, three scenarios were run and evaluated. The scenario assessment showed that it is necessary an integrated approach for the water management of the Drava river, which considers an upgrading of the WWTP with Nitrogen and Phosphorous removal and the treatment of other diffuse pollution and point sources (including the overflow of the WWTP). Additionally, if an increase in the minimum instream flow after the dams is considered, a higher dilution capacity and a higher self-cleaning capability could be obtained. The results proved that integrated models like the one presented here have an added value for decision support in water management. This kind of integrated approach is useful to get insight in aquatic ecosystems, for assessing investments in sanitation infrastructure of urban wastewater systems considering both, the fulfilling of legal physical chemical emission limits and the ecological state of the receiving waters

    Software defect prediction: do different classifiers find the same defects?

    Get PDF
    Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio
    • …
    corecore