Resilient Computing Curriculum

This Deliverable presents the MSc Curriculum in Resilient Computing suggested by ReSIST. It includes the description of the syllabi for all the courses in the two semesters of the first year, those for the common courses in semester 3 in the second year together with an exemplification of possible application tracks with the related courses. This MSc curriculum has been updated and completed taking advantage of a large open discussion inside and outside ReSIST. This MSc Curriculum is on-line on the official ReSIST web site, where all information is available together with all the support material generated by ReSIST and all other relevant freely available support material.European Commission through NoE IST-4-026764-NOE (ReSIST

Prediksi Reliabilitas Perangkat Lunak Menggunakan Support Vector Regression dan Model Mining

Reliabilitas perangkat lunak didefinisikan sebagai probabilitas operasi perangkat lunak yang bebas dari kegagalan (failure) dalam sebuah periode waktu tertentu. Pemodelan reliabilitas perangkat lunak ini dapat dilakukan salah satunya dengan memanfaatkan data kegagalan perangkat lunak untuk melakukan prediksi kegagalan di masa datang. Salah satu arsitektur yang dipakai dalam pemodelan ini pada umumnya adalah dengan menggunakan beberapa data terakhir untuk melakukan prediksi. Padahal, kegagalan perangkat lunak dapat saja dipengaruhi oleh data yang terdahulu seperti yang telah dibuktikan pada satu penelitian, yang menggunakan teknik model mining untuk memilih data masukan terdahulu tersebut. Pada penelitian ini, diusulkan penggunaan Binary Particle Swarm Optimization (BPSO) sebagai metode model mining untuk melakukan prediksi reliabilitas perangkat lunak dengan menggunakan Support Vector Regression (SVR). Data yang dipakai atau tidak dipakai masing-masing disimbolkan dengan angka “1” atau “0” dan metode ini diujicobakan pada 6 data dari proyek perangkat lunak yang nyata, yaitu data FC1, FC2, FC3, TBF1, TBF2, dan TBF3. Keakuratan model yang diusulkan dibandingkan dengan prediksi yang tidak menggunakan model mining dengan mengukur nilai Mean Squared Error (MSE) dan Average Relative Prediction Error (AE). Metode SVR-BPSO yang diusulkan terbukti dapat menghasilkan prediksi yang lebih akurat, terutama untuk data FC1, FC2, dan FC3 yang bersifat stabil. Sifat data TBF yang berbeda dengan data FC menunjukkan bahwa data ini tidak cocok digunakan sebagai bahan uji coba metode yang diusulkan karena time-between-failure pada data tidak bergantung pada urutan kegagalan tertentu, seperti yang terlihat pada data TBF1, TBF2, dan TBF3. Pemilihan parameter SVR juga mempengaruhi keakuratan prediksi, dimana hal ini dapat diperbaiki pada penelitian selanjutnya. Secara umum, metode yang diusulkan telah dapat menghasilkan prediksi reliabilitas perangkat lunak dengan baik dan penggunaan model mining terbukti dapat memberikan manfaat yang nyata dalam bidang prediksi reliabilitas perangkat lunak. ================================================================= Software reliability is defined as the pobability of failure-free software operation in certain period of time. The modelling of software reliability can be done in one way by using software failure data to predict the future failures. One architecture in this modelling is done generally by using the last few consecutive data to predict the future value, where actually the failure of a software can be dependent also to earlier data as showed in one research about the use of model mining to determine which data to use as prediction. In this research, we propose the use of Binary Particle Swarm Optimization (BPSO) as a model mining method to predict the reliability of software by using Support Vector Regression (SVR) as predictor. To determine which data to use in model mining, the data is symbolized with one “1” or “0” in the structure of BPSO particle. The proposed method is tested with 6 real data from real project, which are called FC1, FC2, FC3, TBF1, TBF2, and TBF3. The accuracy of the proposed model is compared with a predictor without model mining by computing the Mean Squared Error (MSE) and Average Relative Prediction Error (AE). The proposed SVR-BPSO method is proved to be able to predict more accurately, especially in FC1, FC2, and FC3 data which are more stable in nature. The use of TBF data sets proved to be inappropriate as it yields poor prediction results in TBF1, TBF2, and TBF3 data, which may have rooted from the differing nature with FC data. The method to choose SVR parameters can also affect the accuracy of prediction, which opens room for improvement in future research. In general, the proposed method is able to predict the reliability of a software and the use of model mining is important in effort to produce more accurate prediction in software failure data

Geometric Approaches to Statistical Defect Prediction and Learning

Software quality is directly correlated with the number of defects in software systems. As the complexity of software increases, manual inspection of software becomes prohibitively expensive. Thus, defect prediction is of paramount importance to project managers in allocating the limited resources effectively as well as providing many advantages such as the accurate estimation of project costs and schedules. This thesis addresses the issues of defect prediction and learning in the geometric framework using statistical quality control and genetic algorithms. A software defect prediction model using the geometric concept of operating characteristic curves is proposed. The main idea behind this predictor is to use geometric insight in helping construct an efficient prediction method to reliably predict the cumulative number of defects during the software development process. The performance of the proposed approach is validated on real data from actual software projects, and the experimental results demonstrate a much improved performance of the proposed statistical method in predicting defects. In the same vein, two defect learning predictors based on evolutionary algorithms are also proposed. These predictors use genetic programming as feature constructor method. The first predictor constructs new features based primarily on the geometrical characteristics of the original data. Then, an independent classifier is applied and the performance of feature selection method is measured. The second predictor uses a built-in classifier which automatically gets tuned for the constructed features. Experimental results on a NASA static metric dataset demonstrate the feasibility of the proposed genetic programming based approaches

Resource allocation and optimal release time in software systems

Software quality is directly correlated with the number of defects in software systems. As the complexity of software increases, manual inspection of software becomes prohibitively expensive. Thus, defect prediction is of paramount importance to project managers in allocating the limited resources effectively as well as providing many advantages such as the accurate estimation of project costs and schedules. This thesis addresses the issues of statistical fault prediction modeling, software resource allocation, and optimal software release and maintenance policy. A software defect prediction model using operating characteristic curves is presented. The main idea behind this predictor is to use geometric insight in helping construct an efficient prediction method to reliably predict the cumulative number of defects during the software development process. Motivated by the widely used concept of queue models in communication systems and information processing systems, a resource allocation model which answers managerial questions related to project status and scheduling is then introduced. Using the proposed allocation model, managers will be more certain about making resource allocation decisions as well as measuring the system reliability and the quality of service provided to customers in terms of the expected response time. Finally, a novel stochastic model is proposed to describe the cost behavior of the operation, and estimate the optimal time by minimizing a cost function via artificial neural networks. Further, a detailed analysis of software release time and maintenance decision is also presented. The performance of the proposed approaches is validated on real data from actual SAP projects, and the experimental results demonstrate a compelling motivation for improved software qualit

Resilient Computing Courseware

This Deliverable describes the courseware in support to teaching Resilient Computing in a Curriculum for an MSc track following the scheme of the Bologna process. The development of the supporting material for such a curriculum has required a rather intensive activity that involved not only the partners in ReSIST but also a much larger worldwide community with the aim of identifying available updated support material that can be used to build a progressive and methodical line of teaching to accompany students and interested persons in a profitable learning process. All this material is on-line on the official ReSIST web site http://www.resistnoe.org/, can be viewed and downloaded for use in a class and constitutes, at our knowledge, the first, almost comprehensive attempt, to build a database of support material related to Dependable and Resilient Computing.European Commission through NoE IST-4-026764-NOE (ReSIST

Change Request Prediction and Effort Estimation in an Evolving Software System

Prediction of software defects has been the focus of many researchers in empirical software engineering and software maintenance because of its significance in providing quality estimates from the project management perspective for an evolving legacy system. Software Reliability Growth Models (SRGM) have been used to predict future defects in a software release. Modern software engineering databases contain Change Requests (CR), which include both defects and other maintenance requests. Our goal is to use defect prediction methods to help predict CRs in an evolving legacy system. Limited research has been done in defect prediction using curve-fitting methods evolving software systems, with one or more change-points. Curve-fitting approaches have been successfully used to select a fitted reliability model among candidate models for defect prediction. This work demonstrates the use of curve-fitting defect prediction methods to predict CRs. It focuses on providing a curve-fit solution that deals with evolutionary software changes but yet considers long-term prediction of data in the full release. We compare three curve-fit solutions in terms of their ability to predict CRs. Our data show that the Time Transformation approach (TT) provides more accurate CR predictions and fewer under-predicted Change Requests than the other curve-fitting methods. In addition to CR prediction, we investigated the possibility of estimating effort as well. We found Lines of Code (added, deleted, modified, and auto-generated) associated with CRs do not necessarily predict the actual effort spent on CR resolution

Software reliability prediction : a multi-modelling approach

Many software reliability models are now available to the user who wishes to assess and make predictions about the future reliability of his/her system by using its past failure behaviour. Experience of applying such models to failure data in the past has shown that, to date, there is no one model that will give accurate predictions in all circumstances (i. e., over different data sets). Recent work has thus concentrated on the development of techniquesf or the assessmenot f the accuracyo f predictions madef or the data of interest. One of these techniques also allows the user to improve initially inaccurate predictions via a process of recalibration. The demonstrated success of the recalibration technique suggests that it may be possible to apply fairly simple models, for example nonparametric models, and to achieve reliability predictions which are as accurate as those which could be obtainedf rom more sophisticated models.Here, it is recommended that a "multi-modelling" approach should be taken to the problem of software reliability prediction. That is, a number of parametric and nonparametric models and the recalibration technique should be applied to the failure data from the system of interest and by using the various analysis techniques accurate predictions may be selected for the future failure behaviour of this system, from amongst all the resulting "prediction systems". This thesis gives guidance on how such an approach should be taken and validates the approach by application of these methods to some real software failure data. In order to minimise the effort on the part of the user, the feasibility of automating such selection between prediction systems is also investigated. The general conclusion of this work is that the "multi-modelling" approach suggested is effective, in terms of obtaining, fairly automatically, reliability predictions which can be trusted for each data source. We make recommendations on how to minimise effort on the part of the user of such techniques, by more intelligent choice of initial software reliability models, application of subsequentte chniquesf or improvement in the accuracy of predictions and automatic selection from amongst the available prediction systems. There is evidence here that we could apply a small number of raw models (some parametric and some non-parametric), the recalibration technique, and finally the metapredictor for automatically choosing the best predictions, and the resulting predictions will probably be about the same in accuracy as any single predictor that could have been used

Studio longitudinale dell'evoluzione del tropismo per X4/R5, dei livelli di HIV-1 DNA cellulare, dei parametri immunologici e della viremia residua in una popolazione di pazienti naive trattati con successo

Background: Nowadays HIV-1 RNA levels and CD4+ T lymphocyte counts are the standard markers used in clinical practice for the management of HIV infection. However the evolution of HIV infection can be monitored also by measuring HIV-DNA and this measurement can be determined in PBMCs, even during powerful and prolonged antiretroviral therapy. In successfull treated HIV-1 patients, viral load is undetectable and the strategies for managing long-term side effects may involve a new class of antiretroviral-like CCR5 antagonists. Moreover the dynamics and the influence of viral tropism on the course of HIV-1 infection in subjects exposed to antiretroviral therapy are not fully understood. Then the evolution and determination of HIV-1 tropism based on cellular DNA sequence could be useful for patients with a successfully suppressed plasma viral load. Aims: In this study we aimed to determine whether the HIV-1 tropism for CXCR4 or CCR5 correlates with residual viraemia, cellular HIV-1 DNA load and CD4+ count; moreover, we evaluated if exist a correlation between baseline and follow-up HIV-1 DNA levels with residual viraemia, baseline plasma HIV-1 RNA, and the condition of primary or chronic HIV infection at the start of antiretroviral therapy. Methods: In the CAVeAT, that is a prospective cohort of HIV-infected patients enrolled starting from 2004 in five infectious diseases units in Northeastern Italy (Veneto region), we retrospectively selected two subgroup of patients (cohort I and cohort II); they were a subset of subjects achieving virological suppression within 6 months after initiation of first-line therapy and maintaining plasma HIV RNA levels < 50 copies/ml, without virological failures, until evaluation at the follow-up time points. In order to be included in the our study, the patients needed to be naïve and treated with effective antiretroviral therapy. None of the patients were treated with CCR5 antagonists. The cohort I consisted on 219 patients with median follow-up time of 3 years (T0, T1, T2) while the cohort II was represented of 181 patients with median follow-up times of 4 years (T0, T1, T2, T3). Genotypic analysis of viral tropism was performed on PBMCs throught the sequencing of V3 loop of gp120; the generated sequences were interpreted using the bioinformatic tool Geno2pheno coreceptor while proviral DNA was quantified by Real-Time PCR using TaqMan probes. Results: In the cohort I, HIV-1 DNA, CD4+ count and plasma viraemia were available from all 219 patients at T0 and T1, and in 86 subjects at T2, while tropism determinations were available from 109 subjects at T0, 219 at T1, and from 86 subjects at T2. The results showed that achieving a residual viraemia < 2.5 cp/ml at T1 correlated with having the same condition at T2 and that there was a positive correlation between To and T1 -T2 tropism. X4 tropism at T1 negatively correlated with the possibility of achieving viraemia < 2.5 cp/ml at T2 while a positive correlation between viremic suppression and R5 coreceptor affinity was found. In 181 patients of the cohort II, viroimmunological data were collected at baseline (T0) and at two follow-up time points (T1, T2); in a subgroup of 70 subjects, we evaluated also a third follow-up time point (T3). We observed that high baseline plasma HIV-1 RNA values positive correlated with high levels of HIV-1 DNA at T0, T1, T2, T3 and negative correlated with residual viraemia at T1, T2, T3; having high levels of HIV-1 DNA at T0 positive correlated with high values at T1, T2, T3 and negatively correlated with achieving residual viraemia. Primary infection was associated with lower probability of having high HIV-1 DNA levels at T1, T2, T3 and with a higher probability of achieving residual viraemia at T1 and T3, with respect to chronic infection. Conclusions: The tropism of archived virus was stable during an effective treatment, although a low percentage of patients switched over time. R5 tropism and its stability were related to achieving and maintaining viraemia < 2.5 copies/ml, in treatment responder patients, suggesting a relation among viral tropism and response to treatment in the long term. Moreover, we demonstrated a strong and long-lasting correlation between viral load and cellular HIV-1 DNA before and after the start of HAART and that cellular HIV-1 DNA is closely related to residual viraemia over long-term follow-up of ART responders, particularly when treated during primary infection

A Guideline for Environmental Games (GEG) and a randomized controlled evaluation of a game to increase environmental knowledge related to human population growth

People often have very little knowledge about the impact of unsustainable human population growth on the environment and social well-being especially in developing countries. Therefore, an efficient method should be explored in order to educate, and if possible, to convince the members of the public to realize the environmental and social problems caused by the unsustainable population growth. Digital Game-Based Learning (DGBL) has been highlighted by some studies as an innovative tool for learning enhancement. While only a handful of studies have scientifically evaluated the impact of DGBL on knowledge outcomes, the approach is an attractive tool to increase knowledge and motivate engagement with environmental issues surrounding population growth because of its potential to improve learners’ motivation and engagement thereby compared to traditional learning approaches. Therefore, the three primary research questions for this study are: 1) "Can a single-player digital game be an appropriate and attractive learning application for the players to gain insight about the relationship between the growing human population and the environmental issues?" 2) "How can we design environmental games for the players to gain insights about the relationship between the growing human population and the environmental issues via playing a game?" and 3) "What are the obstacles preventing the players from adapting environmental knowledge obtained from the learning mediums into the real-life?" To inform the development of an efficacious DGBL game to impact learning outcomes, critical reviews of environmental issues related to population growth as well as critical reviews of commercial and serious environmental games in terms of their educational and motivational values were undertaken in this study. The results of these critical reviews informed the development of a Guideline for Environmental Games (or GEG). The GEG was developed by combining the engaging game technology with environmental learning and persuasion theories. The GEG was then used to inform the development of a prototype game called THE GROWTH; a single-player, quiz-based, city-management game targeting young adolescents and adults. Multiple evaluation methods of the game were used to answer the three key research questions mentioned earlier. These methods included: 1) The Randomized Controlled Trial approach (RCT) where the participants were systematically divided into the experimental and the control group respectively and their knowledge scores (quantitative data) compared and analyzed, 2) The participants’ abilities to recall and describe the environmental and well-being issues were collected and analyzed qualitatively using The Content Analysis method (CA) and, 3) The participants’ overall feedback on the learning mediums was collected and analyzed to evaluate the motivational values of THE GROWTH itself. To this end, THE GROWTH was evaluated with 82 Thai-nationality participants (70 males and 12 females). The results showed that participants assigned to play THE GROWTH demonstrated greater environmental and social-well-being knowledge related to population growth (F(1,40) = 43.86, p = .006) compared to the control group participants assigned to a non-interactive reading activity (consistent with material presented in THE GROWTH). Furthermore, participants who played THE GROWTH recalled on average more content presented in the game when compared to participants who were presented with similar content in the reading material (t (59) = 3.35, p = .001). In terms of level of engagement, the study suggested that participants assigned to the game were more engaging with their learning medium on average when compared to participants assigned to the non-interactive reading activity. This is evidenced by the longer time participants spent on the task, the activity observed from participants’ recorded gameplay, and their positive responses in the survey. The semi-structured interviews used in this study highlighted the participants’ attitudes towards the environmental, social, and technological issues. Although the participants’ perceived behavioural intention towards the environmental commitments were not statistically differed between the two study group, their responses still provide some evidences that leaps may occur from the learning mediums to the real-world context. Furthermore, these responses can be valuable evidences for the policy makers and for the future development of environmental serious games. Overall, the results suggested that digital environmental games such as THE GROWTH might be an effective and motivational tool in promote the learning about sustainable population size, the environment, and the social well-being. The game’s ability to convince the participants to change towards sustainable lifestyles, however, might be subjected to the future research and other real-world circumstances such as the governmental and public supports. In summary, the research in this thesis makes the following contributions to knowledge: • The Guideline for Environmental Games (GEG) contributes to knowledge about making theoretically-based environmental games. It has particular significance because the guideline was validated by demonstrating learning improvements in a systematic randomized controlled trial. • The use of Multi-Strategy Study Design where multiple systematic evaluation methods were used in conjunction to provide conclusive findings about the efficacy of DGBL to impact outcomes. • THE GROWTH itself is a contribution to applied research as an example of an effective DGBL learning tool