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检索条件"任意字段=9th International Conference on Predictive Models in Software Engineering, PROMISE 2013"

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Are comprehensive quality models necessary for evaluating software quality? 9

Are comprehensive quality models necessary for evaluating so...

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9th international conference on predictive models in software engineering, promise 2013

作者： Lochmann, Klaus Ramadani, Jasmin Wagner, Stefan Technische Universität Mönchen Software and Systems Engineering Munich Germany University of Stuttgart Institute of Software Technology Stuttgart Germany

ISBN: (纸本)9781450320160

the concept of software quality is very complex and has many facets. Reecting all these facets and at the same time measuring everything related to these facets results in comprehensive but large quality models and extensive mea-surements. In contrast, there are also many smaller, focused quality models claiming to evaluate quality with few mea-sures. We investigate if and to what extent it is possible to build a focused quality model with similar evaluation results as a comprehensive quality model but with far less measures needed to be collected and, hence, reduced effort. We make quality evaluations with the comprehensive Quamoco base quality model and build focused quality models based on the same set of measures and data from over 2,000 open source systems. We analyse the ability of the focused model to predict the results of the Quamoco model by comparing them with a random predictor as a baseline. We calculate the standardised accuracy measure SA and effect sizes. We found that for the Quamoco model and its 378 auto-matically collected measures, we can build a focused model with only 10 measures but an accuracy of 61% and a medium to high effect size. We conclude that we can build focused quality models to get an impression of a system's quality similar to comprehensive models. However, when including manually collected measures, the accuracy of the models stayed below 50 %. Hence, manual measures seem to have a high impact and should therefore not be ignored in a focused model.

关键词： Open source software

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Using evidential reasoning to make qualified predictions of software quality 9

Using evidential reasoning to make qualified predictions of ...

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9th international conference on predictive models in software engineering, promise 2013

作者： Walkinshaw, Neil Department of Computer Science University of Leicester United Kingdom

ISBN: (纸本)9781450320160

software quality is commonly characterised in a top-down manner. High-level notions such as quality are decomposed into hierarchies of sub-factors, ranging from abstract no-tions such as maintainability and reliability to lower-level notions such as test coverage or team-size. Assessments of abstract factors are derived from relevant sources of infor-mation about their respective lower-level sub-factors, by sur-veying sources such as metrics data and inspection reports. this can be difficult because (1) evidence might not be avail-able, (2) interpretations of the data with respect to certain quality factors may be subject to doubt and intuition, and (3) there is no straightforward means of blending hierarchies of heterogeneous data into a single coherent and quantita-tive prediction of quality. this paper shows how Evidential Reasoning (ER)-a mathematical technique for reasoning about uncertainty and evidence-can address this problem. It enables the quality assessment to proceed in a bottom-up manner, by the provision of low-level assessments that make any uncertainty explicit, and automatically propagat-ing these up to higher-level 'belief-functions' that accurately summarise the developer's opinion and make explicit any doubt or ignorance.

关键词： Uncertainty analysis

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An algorithmic approach to missing data problem in modeling human aspects in software development 9

An algorithmic approach to missing data problem in modeling ...

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9th international conference on predictive models in software engineering, promise 2013

作者： Calikli, Gul Bener, Ayse Data Science Laboratory Dept. of Mechanical and Industrial Engineering Ryerson University Canada

ISBN: (纸本)9781450320160

Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. this, in turn, leads to an increase in defect density. the performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem.

关键词： Defect density

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An analysis of multi-objective evolutionary algorithms for training ensemble models based on different performance measures in software effort estimation 9

An analysis of multi-objective evolutionary algorithms for t...

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9th international conference on predictive models in software engineering, promise 2013

作者： Minku, Leandro L. Yao, Xin CERCIA School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9781450320160

Background: Previous work showed that Multi-objective Evolutionary Algorithms (MOEAs) can be used for training ensembles of learning machines for software Effort Estima-tion (SEE) by optimising different performance measures concurrently. Optimisation based on three measures (LSD, MMRE and PRED(25)) was analysed and led to promising results in terms of performance on these and other measures. Aims: (a) It is not known how well ensembles trained on other measures would behave for SEE, and whether train-ing on certain measures would improve performance partic-ularly on these measures. (b) It is also not known whether it is best to include all SEE models created by the MOEA into the ensemble, or solely the models with the best train-ing performance in terms of each measure being optimised. Investigating (a) and (b) is the aim of this work. Method: MOEAs were used to train ensembles by optimis-ing four different sets of performance measures, involving a total of nine different measures. the performance of all ensembles was then compared based on all these nine per-formance measures. Ensembles composed of different sets of models generated by the MOEAs were also compared. Results: (a) Ensembles trained on LSD, MMRE and PRED (25) obtained the best results in terms of most performance measures, being considered more successful than the others. Optimising certain performance measures did not necessar-ily lead to the best test performance on these particular mea-sures probably due to overfitting. (b) there was no inherent advantage in using ensembles composed of all the SEE mod-els generated by the MOEA in comparison to using solely the best SEE model according to each measure separately. Conclusions: Care must be taken to prevent overfitting on the performance measures being optimised. Our results sug-gest that concurrently optimising LSD, MMRE and PRED (25) promoted more ensemble diversity than other combina-tions of measures, and hence performed best. Low diversity is mor

关键词： Evolutionary algorithms

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Forecasting software development project characteristics using meta-modeling 13

Forecasting software development project characteristics usi...

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9th Central and Eastern European software engineering conference in Russia, CEE-SECR 2013

作者： Bazhenov, A.S. Itsykson, V.M. CSSE ICC SPbSPU Saint-Petersburg Russia

ISBN: (纸本)9781450326414

Applying forecasting principles to software development projects is becoming one of the main trends in the modern commercial software development. there are several reasons for that, but one of the major is the rapid industry growth, which sets high expectations for the results quality and delivery timelines. Forecasting becomes possible through utilizing modern software development methodologies, simulation and statistical models. this research introduces a method to create custom software development project models (to be further used for forecasting) based on the so-called meta-modeling principles. the article defines the meta-model concept, its structure and principles, gives examples on how it can be used to simulate real software development projects. Copyright is held by the owner/author(s).

关键词： Forecasting

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A hybrid mobility model based on social, cultural and language diversity

A hybrid mobility model based on social, cultural and langua...

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9th IEEE international conference on Collaborative Computing: Networking, Applications and Worksharing, COLLABORATECOM 2013

作者： Karim, Lutful Mahmoud, Qusay H. School of Information and Communications Technology Seneca College of Applied Arts and Science Toronto ON Canada Dept. Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa ON Canada

ISBN: (纸本)9781936968923

Human Mobility models (HMMs) have become increasingly relevant in different application domains including networks designed for data dissemination protocols. Most existing HMMs consider the temporal and spatial features of human mobility. In addition, many recent HMMs based on social networks have begun to take into account social factors, which naturally influence the results of the HMMs. Unfortunately, these mobility models do not focus on the impact of several important social factors that can affect a person's actions, such as language, citizenship, and culture. this paper introduces a hybrid human mobility model that integrates social behavior, cultural and language factors into HMMs in order to measure their effect on human mobility, as well as temporal and spatial features. Results demonstrate the feasibility of the proposed model. © 2013 ICST.

关键词： Social behavior

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Multi-Objective Cross-Project Defect Prediction

Multi-Objective Cross-Project Defect Prediction

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6th IEEE international conference on software Testing, Verification and Validation (ICST)

作者： Canfora, Gerardo De Lucia, Andrea Di Penta, Massimiliano Oliveto, Rocco Panichella, Annibale Panichella, Sebastiano Univ Sannio Via Traiano I-82100 Benevento Italy Univ Salerno I-84084 Fisciano Italy Univ Molise Contrada Fonte Lappone I-86090 Pesche Italy

ISBN: (纸本)9780769549682

Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models. However, existing research suggests that cross-project prediction is particularly challenging and, due to heterogeneity of projects, prediction accuracy is not always very good. this paper proposes a novel, multi-objective approach for cross-project defect prediction, based on a multi-objective logistic regression model built using a genetic algorithm. Instead of providing the software engineer with a single predictive model, the multi-objective approach allows software engineers to choose predictors achieving a compromise between number of likely defect-prone artifacts (effectiveness) and LOC to be analyzed/tested (which can be considered as a proxy of the cost of code inspection). Results of an empirical evaluation on 10 datasets from the promise repository indicate the superiority and the usefulness of the multi-objective approach with respect to single-objective predictors. Also, the proposed approach outperforms an alternative approach for cross-project prediction, based on local prediction upon clusters of similar classes.

关键词： Cross-project defect prediction multi-objective optimization search-based software engineering

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Evaluation of understandability of object-oriented design 9th

Evaluation of understandability of object-oriented design

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9th international conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2013

作者： Soni, Devpriya Department of Computer Science and Engineering Jaypee Institute of Information Technology sec- 128 Noida India

ISBN: (纸本)9783642379482

Quality of software design directly affects the understandability of the software developed. As the size and complexity of the software increases it drastically affects quality attributes, especially understandability. the direct measurement of quality is difficult because there is no single model that can be applied in all situations. models proposed by various researchers are not comprehensive. Quantitative measurement of an operational system's understandability is desirable both as an instantaneous measure and as a predictor of understandability over time. this work proposes the method of measuring understandability using Logical Scoring of Preferences (LSP) method. I have also evaluated one design through this model. © Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2013.

关键词： software design

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the Application of Multi-Model Ensemble Approach as a Prognostic Method to Predict Patient Health Status

The Application of Multi-Model Ensemble Approach as a Progno...

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IEEE international conference on Prognostics and Health Management (PHM)

作者： Ghavami, Peter K. Kapur, Kailash C. Univ Washington Harborview Med Ctr UW Med 325 9Th Ave Seattle WA 98104 USA Univ Washington Dept Ind & Syst Engn Seattle WA 98195 USA

ISBN: (纸本)9781467357227

Prognostic methods promise to improve patient healthcare if predictions of adverse disease and medical complications for each patient can be predicted in advance. Prognostics and prediction of patients' physiological health status are getting attention in medicine because they provide insight that can be used for medical interventions that prevent adverse medical complications. While various predictive analytics have been developed for detection and prediction of certain diseases, efforts to combine the predictive power of multiple algorithms have gone mostly unnoticed. this study proposes a prognostics engine using multiple models to predict patient physiological status. Given the diversity of clinical data and disease conditions, no single model can be the ideal prediction algorithm to cover all medical cases. Certain algorithms are more accurate than others depending on input data available, the type, amount and diversity of possible outcomes. In this study four different neural network algorithms were used for the prognostics engine and their accuracy on a dataset were compared. the study proposes using an ensemble of algorithms and an oracle, an overseer program to select the most accurate combination of the predictive models that is most suited for a particular disease prediction. the feasibility of this approach is tested using a clinical data set of 1,073 patient cases including 255 patients presented with Deep Vein Pulmonary Embolism. the study compared accuracy of five different schemas for constructing ensembles of various neural networks. the multiple schema approach combined with multi-model ensembles showed to improve accuracy of prediction for this case and promises to be a robust approach to other clinical prediction problems.

关键词： diseases health care medical computing neural nets patient care

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the impact of parameter tuning on software effort estimation using learning machines 9

The impact of parameter tuning on software effort estimation...

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9th international conference on predictive models in software engineering, promise 2013

作者： Song, Liyan Minku, Leandro L. Yao, Xin CERCIA School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9781450320160

Background: the use of machine learning approaches for software effort estimation (SEE) has been studied for more than a decade. Most studies performed comparisons of different learning machines on a number of data sets. However, most learning machines have more than one parameter that needs to be tuned, and it is unknown to what extent parameter settings may affect their performance in SEE. Many works seem to make an implicit assumption that parameter settings would not change the outcomes significantly. Aims: To investigate to what extent parameter settings affect the performance of learning machines in SEE, and what learning machines are more sensitive to their parameters. Method: Considering an online learning scenario where learning machines are updated with new projects as they become available, systematic experiments were performed using five learning machines under several different parameter settings on three data sets. Results: While some learning machines such as bagging using regression trees were not so sensitive to parameter settings, others such as multilayer perceptrons were affected dramatically. Combining learning machines into bagging ensembles helped making them more robust against different parameter settings. the average performance of k-NN across different projects was not so much affected by different parameter settings, but the parameter settings that obtained the best average performance across time steps were not so consistently the best throughout time steps as in the other approaches. Conclusions: Learning machines that are more/less sensitive to different parameter settings were identified. the different sensitivity obtained by different learning machines shows that sensitivity to parameters should be considered as one of the criteria for evaluation of SEE approaches. A good learning machine for SEE is not only one which is able to achieve superior performance, but also one that is either less dependent on parameter settings or to which

关键词： Parameter estimation

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