| 1. |
- A.V., Fidalgo, et al.
(författare)
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Adapting remote labs to learning scenarios : Case studies using VISIR and remotElectLab
- 2014
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Ingår i: Revista Iberoamericana de Tecnologias del Aprendizaje. - : Education Society of IEEE (Spanish Chapter). - 1932-8540. ; 9:1, s. 33-39
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Tidskriftsartikel (refereegranskat)abstract
- Remote laboratories are an emergent technological and pedagogical tool at all education levels, and their widespread use is an important part of their own improvement and evolution. This paper describes several issues encountered on laboratorial classes, on higher education courses, when using remote laboratories based on PXI systems, either using the VISIR system or an alternate in-house solution. Three main issues are presented and explained, all reported by teachers, that gave support to students' use of remote laboratories. The first issue deals with the need to allow students to select the actual place where an ammeter is to be inserted on electric circuits, even incorrectly, therefore emulating real-world difficulties. The second one deals with problems with timing when several measurements are required at short intervals, as in the discharge cycle of a capacitor. In addition, the last issue deals with the use of a multimeter in dc mode when reading ac values, a use that collides with the lab settings. All scenarios are presented and discussed, including the solution found for each case. The conclusion derived from the described work is that the remote laboratories area is an expanding field, where practical use leads to improvement and evolution of the available solutions, requiring a strict cooperation and information-sharing between all actors, i.e., developers, teachers, and students.
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| 2. |
- Aaboen, Lise, 1978, et al.
(författare)
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Nourishment for the piggy bank : facilitation of external financing in incubators
- 2011
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Ingår i: International Journal of Technology Transfer and Commercialisation. - : Inderscience. - 1470-6075 .- 1741-5284. ; 10 3-4, s. 354-374
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we argue that incubators facilitate access to external financing for their incubatees. Incubators use a wide range of activities to facilitate the accessing of external financing from public and private sources. We have grouped these into two sets of activities. The general activities aim to develop the conditions for external financing through information, education of incubatees, network-building and lobbying activities. The specific activities aim to assist the individual incubatee in their pursuit of external finance through help in application procedures, establishing need for capital, making contacts with the best public or private investor, etc. Based on the survey data, we have also shown that it is more common for incubatees to attract external capital compared to non-incubator firms. The incubatees seem especially successful in attracting public capital. The incubatees also attract more private external capital, however, the observed frequency of private capital in the incubatees are low.
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| 3. |
- Aceijas, Carmen, et al.
(författare)
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Teaching Ethics in Schools of Public Health in the European Region : Findings from a Screening Survey
- 2012
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Ingår i: Public Health Reviews. - : Public Health Reviews. - 0301-0422 .- 2107-6952. ; 34:1, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- A survey targeting ASPHER members was launched in 2010/11, being a first initiative in improving ethics education in European Schools of Public Health. An 8-items questionnaire collected information on teaching of ethics in public health. A 52% response rate (43/82) revealed that almost all of the schools (95% out of 40 respondents with valid data) included the teaching of ethics in at least one of its programmes. They also expressed the need of support, (e.g.: a model curriculum (n=25), case studies (n=24)), which indicates further work to be met by the ASPHER Working Group on Ethics and Values in Public Health.
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| 4. |
- Adams, Liz, et al.
(författare)
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What It's Like to Participate in an ITiCSE Working Group
- 2011
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Ingår i: ACM SIGCSE Bulletin. - : ACM Press. - 0097-8418. ; 43:1
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Have you ever participated in a working group at ITiCSE? No? Then you have missed a great opportunity to meet and work with a group of like-minded educators on a topic of common interest. The actual meeting at ITiCSE translates to hard work, but also a lot of fun.
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| 5. |
- Afzal, Wasif, et al.
(författare)
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Genetic programming for cross-release fault count predictions in large and complex software projects
- 2010
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Ingår i: Evolutionary Computation and Optimization Algorithms in Software Engineering. - : IGI Global, Hershey, USA. - 9781615208098
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Bokkapitel (refereegranskat)abstract
- Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.
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| 6. |
- Afzal, Wasif, et al.
(författare)
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On the application of genetic programming for software engineering predictive modeling : A systematic review
- 2011
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Ingår i: Expert Systems with Applications. - : Pergamon-Elsevier Science Ltd. - 0957-4174 .- 1873-6793. ; 38:9, s. 11984-11997
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Forskningsöversikt (refereegranskat)abstract
- The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modeling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling: the results are inconclusive for software cost/effort/size estimation.
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| 7. |
- Afzal, Wasif, et al.
(författare)
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Resampling Methods in Software Quality Classification
- 2012
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Ingår i: International Journal of Software Engineering and Knowledge Engineering. - Sweden : World Scientific. - 0218-1940. ; 22:2, s. 203-223
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Tidskriftsartikel (refereegranskat)abstract
- In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use. This is seen as one of the contributing factors for not being able to draw general conclusions on what modeling technique or set of predictor variables are the most appropriate. Furthermore, the use of a variety of resampling methods make it impossible to perform any formal meta-analysis of the primary study results. Therefore, it is desirable to examine the influence of various resampling methods and to quantify possible differences. Objective and method: This study empirically compares five common resampling methods (hold-out validation, repeated random sub-sampling, 10-fold cross-validation, leave-one-out cross-validation and non-parametric bootstrapping) using 8 publicly available data sets with genetic programming (GP) and multiple linear regression (MLR) as software quality classification approaches. Location of (PF, PD) pairs in the ROC (receiver operating characteristics) space and area under an ROC curve (AUC) are used as accuracy indicators. Results: The results show that in terms of the location of (PF, PD) pairs in the ROC space, bootstrapping results are in the preferred region for 3 of the 8 data sets for GP and for 4 of the 8 data sets for MLR. Based on the AUC measure, there are no significant differences between the different resampling methods using GP and MLR. Conclusion: There can be certain data set properties responsible for insignificant differences between the resampling methods based on AUC. These include imbalanced data sets, insignificant predictor variables and high-dimensional data sets. With the current selection of data sets and classification techniques, bootstrapping is a preferred method based on the location of (PF, PD) pair data in the ROC space. Hold-out validation is not a good choice for comparatively smaller data sets, where leave-one-out cross-validation (LOOCV) performs better. For comparatively larger data sets, 10-fold cross-validation performs better than LOOCV.
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| 8. |
- Afzal, Wasif, et al.
(författare)
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Search-based prediction of fault-slip-through in large software projects
- 2010
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Ingår i: Proceedings - 2nd International Symposium on Search Based Software Engineering, SSBSE 2010. - : IEEE. - 9780769541952 ; , s. 79-88
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Konferensbidrag (refereegranskat)abstract
- A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.
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| 9. |
- Afzal, Wasif
(författare)
-
Search-Based Prediction of Software Quality : Evaluations and Comparisons
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software verification and validation (V&V) activities are critical for achieving software quality; however, these activities also constitute a large part of the costs when developing software. Therefore efficient and effective software V&V activities are both a priority and a necessity considering the pressure to decrease time-to-market and the intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions that affects software quality, e.g., how to allocate testing resources, develop testing schedules and to decide when to stop testing, needs to be as stable and accurate as possible. The objective of this thesis is to investigate how search-based techniques can support decision-making and help control variation in software V&V activities, thereby indirectly improving software quality. Several themes in providing this support are investigated: predicting reliability of future software versions based on fault history; fault prediction to improve test phase efficiency; assignment of resources to fixing faults; and distinguishing fault-prone software modules from non-faulty ones. A common element in these investigations is the use of search-based techniques, often also called metaheuristic techniques, for supporting the V&V decision-making processes. Search-based techniques are promising since, as many problems in real world, software V&V can be formulated as optimization problems where near optimal solutions are often good enough. Moreover, these techniques are general optimization solutions that can potentially be applied across a larger variety of decision-making situations than other existing alternatives. Apart from presenting the current state of the art, in the form of a systematic literature review, and doing comparative evaluations of a variety of metaheuristic techniques on large-scale projects (both industrial and open-source), this thesis also presents methodological investigations using search-based techniques that are relevant to the task of software quality measurement and prediction. The results of applying search-based techniques in large-scale projects, while investigating a variety of research themes, show that they consistently give competitive results in comparison with existing techniques. Based on the research findings, we conclude that search-based techniques are viable techniques to use in supporting the decision-making processes within software V&V activities. The accuracy and consistency of these techniques make them important tools when developing future decision-support for effective management of software V&V activities.
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| 10. |
- Afzal, Wasif
(författare)
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Using faults-slip-through metric as a predictor of fault-proneness
- 2010
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE. - 9780769542669
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Konferensbidrag (refereegranskat)abstract
- The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the specific test levels of integration and system test. We applied eight classification techniques to the task of identifying fault-prone modules, representing a variety of approaches, including a standard statistical technique for classification (logistic regression), tree-structured classifiers (C4.5 and random forests), a Bayesian technique (Na\"{i}ve Bayes), machine-learning techniques (support vector machines and back-propagation artificial neural networks) and search-based techniques (genetic programming and artificial immune recognition systems) on FST data collected from two large industrial projects from the telecommunication domain. \emph{Results:} Using area under the receiver operating characteristic (ROC) curve and the location of (PF, PD) pairs in the ROC space, GP showed impressive results in comparison with other techniques for predicting fault-prone modules at both integration and system test levels. The use of faults-slip-through metric in general provided good prediction results at the two test levels. The accuracy of GP is statistically significant in comparison with majority of the techniques for predicting fault-prone modules at integration and system test levels. (ii) Faults-slip-through metric has the potential to be a generally useful predictor of fault-proneness at integration and system test levels.
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