During software development and maintenance, as a software system evolves, changes are made and bugs are fixed in various files. In large-scale systems, file histories are stored in software repositories, such as CVS, which record modifications. By studying software repositories, we can learn about open source software development rocesses. Knowing where these changes will happen in advance, gives power to managers and developers to concentrate on those files. Due to the unpredictability in software development process, proposing an accurate change prediction model is hard. It is even harder to compare different models with the actual model of changes that is not available. <br /><br /> In this thesis, we first analyze the information generated during the development process, which can be obtained through mining the software repositories. We observe that the change data follows a Zipf distribution and exhibits self-similarity. Based on the extracted data, we then develop three probabilistic models to predict which files will have changes or bugs. One purpose of creating these models is to rank the files of the software that are most susceptible to having faults. <br /><br /> The first model is Maximum Likelihood Estimation (MLE), which simply counts the number of events i. e. , changes or bugs that occur in to each file, and normalizes the counts to compute a probability distribution. The second model is Reflexive Exponential Decay (RED), in which we postulate that the predictive rate of modification in a file is incremented by any modification to that file and decays exponentially. The result of a new bug occurring to that file is a new exponential effect added to the first one. The third model is called RED Co-Changes (REDCC). With each modification to a given file, the REDCC model not only increments its predictive rate, but also increments the rate for other files that are related to the given file through previous co-changes. <br /><br /> We then present an information-theoretic approach to evaluate the performance of different prediction models. In this approach, the closeness of model distribution to the actual unknown probability distribution of the system is measured using cross entropy. We evaluate our prediction models empirically using the proposed information-theoretic approach for six large open source systems. Based on this evaluation, we observe that of our three prediction models, the REDCC model predicts the distribution that is closest to the actual distribution for all the studied systems.

During software development and maintenance, as a software system evolves, changes are made and bugs are fixed in various files. In large-scale systems, file histories are stored in software repositories, such as CVS, which record modifications. By studying software repositories, we can learn about open source software development rocesses. Knowing where these changes will happen in advance, gives power to managers and developers to concentrate on those files. Due to the unpredictability in software development process, proposing an accurate change prediction model is hard. It is even harder to compare different models with the actual model of changes that is not available. <br /><br /> In this thesis, we first analyze the information generated during the development process, which can be obtained through mining the software repositories. We observe that the change data follows a Zipf distribution and exhibits self-similarity. Based on the extracted data, we then develop three probabilistic models to predict which files will have changes or bugs. One purpose of creating these models is to rank the files of the software that are most susceptible to having faults. <br /><br /> The first model is Maximum Likelihood Estimation (MLE), which simply counts the number of events i. e. , changes or bugs that occur in to each file, and normalizes the counts to compute a probability distribution. The second model is Reflexive Exponential Decay (RED), in which we postulate that the predictive rate of modification in a file is incremented by any modification to that file and decays exponentially. The result of a new bug occurring to that file is a new exponential effect added to the first one. The third model is called RED Co-Changes (REDCC). With each modification to a given file, the REDCC model not only increments its predictive rate, but also increments the rate for other files that are related to the given file through previous co-changes. <br /><br /> We then present an information-theoretic approach to evaluate the performance of different prediction models. In this approach, the closeness of model distribution to the actual unknown probability distribution of the system is measured using cross entropy. We evaluate our prediction models empirically using the proposed information-theoretic approach for six large open source systems. Based on this evaluation, we observe that of our three prediction models, the REDCC model predicts the distribution that is closest to the actual distribution for all the studied systems.

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Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-25T18:13:09Z No. of bitstreams: 2 Tese - Luis J. E. R. Cabrejos.pdf: 5126285 bytes, checksum: b667aa9fb7c3b14f995507bb4a821f1e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-25T18:13:30Z (GMT) No. of bitstreams: 2 Tese - Luis J. E. R. Cabrejos.pdf: 5126285 bytes, checksum: b667aa9fb7c3b14f995507bb4a821f1e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2017-08-25T18:13:48Z (GMT) No. of bitstreams: 2 Tese - Luis J. E. R. Cabrejos.pdf: 5126285 bytes, checksum: b667aa9fb7c3b14f995507bb4a821f1e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-08-25T18:13:48Z (GMT). No. of bitstreams: 2 Tese - Luis J. E. R. Cabrejos.pdf: 5126285 bytes, checksum: b667aa9fb7c3b14f995507bb4a821f1e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-06-08 / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / User eXperience (UX) refers to a holistic perspective and an enrichment of traditional quality models with non-utilitarian concepts, such as fun, joy, pleasure, hedonic value or ludic value. In order to evaluate UX in software applications, several technologies (tools, methods, techniques) have been proposed that range from using questionnaires to employing biometrics to gather quantitative and qualitative data on users’ experience. However, there is a need for research in the development of specific UX evaluation technologies that are easy and comfortable to use from the point of view of users, while supporting software engineers in the correction of the aspects that cause poor experiences. Additionally, new UX approaches should be proposed for evaluating mobile applications, as there is still a shortage of methods for this type of applications, which is rising in demand. This doctoral dissertation proposes an alternative approach for evaluating mobile applications called Redesigning for EXperience (REX), which intends to be less intrusive for users when extracting UX data, while generating reports containing design suggestions for improving the UX. We assessed the acceptance of the initial versions of the REX approach from the point of view of users and software engineers in two studies. When compared to 3E, a qualitative UX evaluation method, the results showed that REX was perceived as more fun, useful and more interactive. Additionally, software engineers considered REX useful and easy to understand, while suggesting improving its report to facilitate its understanding and increase its use. After working on the improvements opportunities from the empirical studies, we developed a tool support for the REX approach called the REX report generator. Also, we carried out an observational study to verify to which extent the REX approach could be applied in a real software development project. Thus, REX was employed by users to evaluate a mobile educational application and a discussion meeting was held with the software development team to discuss the improvement suggestions provided by REX to support the redesign process. The findings from the observational study indicated the satisfaction of users to report their experience with the REX approach, while the members of the development team agreed with the usefulness of the REX report and its improvement suggestions. By providing design suggestions, we aim to support software engineers in improving the UX of the developed mobile applications, thus increasing their quality and acceptance in the market. / .