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Analysing defect inflow distribution of automotive software projects 14

Analysing defect inflow distribution of automotive software ...

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10th international conference on predictive models in software engineering, promise 2014

作者： Rana, Rakesh Staron, Miroslaw Berger, Christian Hansson, Jörgen Nilsson, Martin Computer Science and Engineering Chalmers/ University of Gothenburg Sweden Volvo Car Group Göteborg Sweden

ISBN: (纸本)9781450328982

Defects are real and observable indicators of software quality that can be analyzed and modelled to track the quality and reliability of software system during development and testing. A number of software reliability growth models (SRGMs) have been introduced and evaluated which are based on different family of distributions such as exponential, Weibull, Non-Homogeneous Poisson Process etc. there exist no standard way of selecting the most appropriate SRGMs for given defect data and further the distribution of defect inflow for real software projects from different industrial domains is also not well documented. In this paper we explore the defect inflow distribution of four large software projects from the automotive domain. We evaluate six standard distributions for their ability to fit the defect inflow data and also assess which information criterion is practical for selecting the distribution with best fit. Our results show that beta distribution provides the best fit to the defect inflow data from all projects with different distribution characteristics. Finding the underlying distribution of defect inflow not only help applying the appropriate statistical techniques for data analysis but also to select the appropriate SRGMs for modelling reliability. the information about defect inflow distribution is further useful for modelling the prior beliefs or experience as prior probabilities in Bayesian analysis. Copyright 2014 ACM.

关键词： Defects

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On the influence of maintenance activity types on the issue resolution time 14

On the influence of maintenance activity types on the issue ...

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10th international conference on predictive models in software engineering, promise 2014

作者： Murgia, Alessandro Concas, Giulio Tonelli, Roberto Ortu, Marco Demeyer, Serge Marchesi, Michele University of Antwerp Antwerp Belgium University of Cagliari Italy Cagliari Italy

ISBN: (纸本)9781450328982

the ISO/IEC 14764 standard specifies four types of software maintenance activities spanning the different motivations that software engineers have while performing changes to an existing software system. Undoubtedly, this classification has helped in organizing the workow within software projects, however for planning purposes the relative time differences for the respective tasks remains largely unexplored. In this empirical study, we investigate the inuence of the maintenance type on issue resolution time. From Github's issue repository, we analyze more than 14000 issue reports taken from 34 open source projects and classify them as corrective, adaptive, perfective or preventive maintenance. Based on this data, we show that the issue resolution time depends on the maintenance type. Moreover, we propose a statistical model to describe the distribution of the issue resolution time for each type of maintenance activity. Finally, we demonstrate the usefulness of this model for scheduling the maintenance workload. Copyright 2014 ACM.

关键词： Open source software

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Social metrics included in prediction models on software engineering: A mapping study 14

Social metrics included in prediction models on software eng...

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10th international conference on predictive models in software engineering, promise 2014

作者： Wiese, Igor Scaliante Côgo, Filipe Roseiro Ré, Reginaldo Steinmacher, Igor Gerosa, Marco Aurélio Brazil Brazil

ISBN: (纸本)9781450328982

Context: Previous work that used prediction models on software engineering included few social metrics as predictors, even though many researchers argue that software engineering is a social activity. Even when social metrics were considered, they were classified as part of other dimensions, such as process, history, or change. Moreover, few papers report the individual effects of social metrics. thus, it is not clear yet which social metrics are used in prediction models and what are the results of their use in different contexts. Objective: To identify, characterize, and classify social metrics included in prediction models reported in the literature. Method: We conducted a mapping study (MS) using a snowballing citation analysis. We built an initial seed list adapting strings of two previous systematic reviews on software prediction models. After that, we conducted backward and forward citation analysis using the initial seed list. Finally, we visited the profile of each distinct author identified in the previous steps and contacted each author that published more than 2 papers to ask for additional candidate studies. Results: We identified 48 primary studies and 51 social metrics. We organized the metrics into nine categories, which were divided into three groups-communication, project, and commit-related. We also mapped the applications of each group of metrics, indicating their positive or negative effects. Conclusions: this mapping may support researchers and practitioners to build their prediction models considering more social metrics Copyright 2014 ACM.

关键词： software engineering

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Benchmarking effort estimation models using Archetypal Analysis 14

Benchmarking effort estimation models using Archetypal Analy...

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10th international conference on predictive models in software engineering, promise 2014

作者： Mittas, Nikolaos Karpenisi, Vagia Angelis, Lefteris Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781450328982

the research on software cost estimation has resulted not only to a large number of prediction methodologies and improvement techniques, but also to numerous methods for evaluating and comparing them. the identification of the best prediction model for a specific dataset is still an open issue since the evaluation of candidate models is essentially a multi-criteria problem. Model comparison usually involves statistical hypothesis tests with respect to a single criterion, while for multiple criteria, aggregating methods are usually employed. In the current study, we investigate the alternative approach of benchmarking, which is different from model comparison. the general idea is first to choose among the competitors few "reference models" with special, preferably divergent performance characteristics with respect to multiple criteria and then to examine the placement of all the other models in relation to the reference ones. For solving this problem, we utilize a multivariate statistical method, known as Archetypal Analysis (AA), which provides an appealing and intuitive approach for the identification of the reference models and the subsequent benchmarking of all the competitors. the competitor models are considered as points in a multi-dimensional space, defined by the prediction performance criteria, while AA locates the archetypes, i.e. the reference models which determine the convex hull of the swarm of all points (competitors). Apart from identifying reference models for benchmarking with superior or inferior predictive power according to several accuracy measures, the proposed methodology utilizes the similarity of a subset of models to a "superior" archetype in order to provide a mechanism for building ensembles. the proposed methodology is applied to a dataset containing performance measures of seventy five models which were initially trained and tested on 195 Web projects of the TUKUTUKU database. the application illustrates the straightforwardness and the

关键词： Benchmarking

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Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation 14

Feature weighting techniques for CBR in software effort esti...

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10th international conference on predictive models in software engineering, promise 2014

作者： Sigweni, Boyce Shepperd, Martin Department of Computer Science Brunel University London UB8 3PH United Kingdom

ISBN: (纸本)9781450328982

Context : software effort estimation is one of the most important activities in the software development process. Unfortunately, estimates are often substantially wrong. Numerous estimation methods have been proposed including Case-based Reasoning (CBR). In order to improve CBR estimation accuracy, many researchers have proposed feature weighting techniques (FWT). Objective: Our purpose is to systematically review the empirical evidence to determine whether FWT leads to improved predictions. In addition we evaluate these techniques from the perspectives of (i) approach (ii) strengths and weaknesses (iii) performance and (iv) experimental evaluation approach including the data sets used. Method: We conducted a systematic literature review of published, refereed primary studies on FWT (2000-2014). Results: We identified 19 relevant primary studies. these reported a range of different techniques. 17 out of 19 make benchmark comparisons with standard CBR and 16 out of 17 studies report improved accuracy. Using a one-sample sign test this positive impact is significant (p = 0:0003). Conclusion: the actionable conclusion from this study is that our review of all relevant empirical evidence supports the use of FWTs and we recommend that researchers and practitioners give serious consideration to their adoption. Copyright 2014 ACM.

关键词： Case based reasoning

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the potential benefit of relevance vector machine to software effort estimation 14

The potential benefit of relevance vector machine to softwar...

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10th international conference on predictive models in software engineering, promise 2014

作者： Song, Liyan Minku, Leandro L. Yao, Xin CERCIA School of Computer Science University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom

ISBN: (纸本)9781450328982

three key challenges faced by the task of software effort estimation (SEE) when using predictive models are: (1) in order to support decision-making, software managers should have access not only to the effort estimation given by the predictive model, but also how confident this model is in estimating a given project and how likely other effort values could be the real efforts required to develop this project, (2) SEE data is likely to contain noise, due to the participation of humans in the data collection, and this noise can hinder predictions if not catered, and (3) data collection is an expensive task, and guidelines on when new data need to be collected would be helpful for reducing the cost associated with data collection. However, even though SEE has been studied for decades and many predictors have been proposed, few methods focus on these issues. In this work, we show that relevance vector machine (RVM) is a promising predictive method for addressing these three challenges. More specifically, it explicitly handles noise, it provides probabilistic predictions of effort, and can be used to identify when the required efforts of new projects should be collected for using them as training examples. With that in mind, this work provides the first step in exploiting RVM's potential for SEE by validating both its point prediction and prediction intervals. It then explains in detail future directions in terms of how RVMs can be further exploited for addressing the above mentioned challenges. Our systematic experiments show that RVM is very competitive compared with state-of-the-art SEE approaches, being usually ranked the first or second in 7 across 11 data sets in terms of mean absolute error. We also demonstrate how RVM can be used to judge the amount of noise present in the data. In summary, we show that RVM is a very promising predictor for SEE and should be further exploited. Copyright is held by the owner/author(s).

关键词： Forecasting

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Dealing with uncertainties in the performance modelling of software systems 14

Dealing with uncertainties in the performance modelling of s...

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10th international ACM SIGSOFT conference on Quality of software Architectures, QoSA 2014

作者： Perez-Palacin, Diego Mirandola, Raffaela Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (纸本)9781450325769

models play a central role in the assessment of software non-functional properties like performance and reliability. models can be used both in the initial phases of development to support the designer decisions and at runtime to evaluate the impact of changes in the existing software. However, being abstraction, the models include perse a certain degree of uncertainty. Nevertheless, often this aspect is neglected and models are used beyond their capabilities. Recognising the presence of uncertainties and managing them, would increase the level of trust in a given software model. In this paper we exploit a recently defined taxonomy that classifies the different types of uncertainties and we define a method that, starting from a given model, helps in recognising the existence of uncertainty, in classifying and managing it. We show the method at work on an example application considering the performance of the application as target non-functional property. Copyright © 2014 ACM 978-1-4503-2577-6/14/06 ...$15.00.

关键词： Uncertainty analysis

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Failure data collection for reliability prediction models: A survey 14

Failure data collection for reliability prediction models: A...

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10th international ACM SIGSOFT conference on Quality of software Architectures, QoSA 2014

作者： Buhnova, Barbora Chren, Stanislav Fabriková, Lucie Masaryk University Brno Czech Republic

ISBN: (纸本)9781450325769

Design decisions made early in software development have great impact on the software product quality. Design-time reliability prediction is one of the techniques that support software engineers in early design decisions, based on the evaluation of reliability impact of the individual design alternatives. the accuracy of reliability prediction is critically dependent on the accuracy of reliability prediction models, which relies on uncertain failure parameters (such as the failure probability of component-internal actions). Although the effectiveness of the failure-parameter estimation critically influences the usability of the prediction techniques, the parameter estimation often relies on expert knowledge and is not receiving systematic attention. this paper aims to survey existing techniques for estimation and collection of failure parameters in architecture-based reliability prediction models, and presents the findings that can be learned from their detailed analysis. Copyright © 2014 ACM 978-1-4503-2577-6/14/06 ...$15.00.

关键词： Surveys

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Effort estimation in Agile software Development: A systematic literature review 14

Effort estimation in Agile Software Development: A systemati...

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10th international conference on predictive models in software engineering, promise 2014

作者： Usman, Muhammad Mendes, Emilia Weidt, Francila Britto, Ricardo Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Federal University of Juiz de Fora Brazil

ISBN: (纸本)9781450328982

Context: Ever since the emergence of agile methodologies in 2001, many software companies have shifted to Agile software Development (ASD), and since then many studies have been conducted to investigate effort estimation within such context;however to date there is no single study that presents a detailed overview of the state of the art in effort estimation for ASD. Objectives: the aim of this study is to provide a detailed overview of the state of the art in the area of effort estimation in ASD. Method: To report the state of the art, we conducted a systematic literature review in accordance with the guidelines proposed in the evidence-based software engineering literature. Results: A total of 25 primary studies were selected;the main findings are: i) Subjective estimation techniques (e.g. expert judgment, planning poker, use case points estimation method) are the most frequently applied in an agile context;ii) Use case points and story points are the most frequently used size metrics respectively;iii) MMRE (Mean Magnitude of Relative Error) and MRE (Magnitude of Relative Error) are the most frequently used accuracy metrics;iv) team skills, prior experience and task size are cited as the three important cost drivers for effort estimation in ASD;and v) Extreme Programming (XP) and SCRUM are the only two agile methods that are identified in the primary studies. Conclusion: Subjective estimation techniques, e.g. expert judgment-based techniques, planning poker or the use case points method, are the one used the most in agile effort estimation studies. As for the size metrics, the ones that were used the most in the primary studies were story points and use case points. Several research gaps were identified, relating to the agile methods, size metrics and cost drivers, thus suggesting numerous possible avenues for future work. Copyright 2014 ACM.

关键词： software design

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New models of Coupled Active Materials for Finite Element Package ACELAN 10

New Models of Coupled Active Materials for Finite Element Pa...

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10th international conference on Mathematical Problems in engineering, Aerospace and Sciences (ICNPAA)

作者： Nasedkin, A. V. Skaliukh, A. S. Soloviev, A. N. Southern Fed Univ Rostov Na Donu 344090 Russia

ISBN: (纸本)9780735412767

the paper presents the mathematical models for computational finite element package ACELAN V14. New release of ACELAN is focused not only on the composite elastic, piezoelectric and acoustic media, but also supplemented by more general materials with connectivity of mechanical, electrical, magnetic and thermal fields. In addition, for simulation of nanoscale bodies the different surface effects were added to account in boundary conditions.

关键词： coupled problems piezoelectricity active materials magnetoelectric composite nanoeffects acoustic-structure interface software finite element technologies large sparse matrices quasidefinite matrices

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