Software maintenance reportedly accounts for much of the total cost associated with developing software. These costs occur because modifying software is a highly error-prone task. Changing software to correct faults or add new functionality can cause existing functionality to regress, introducing new faults. To avoid such defects, one can re-test software after modifications, a task commonly known as regression testing. Regression testing typically involves the re-execution of test cases developed for previous versions. Re-running all existing test cases, however, is often costly and sometimes even infeasible due to time and resource constraints. Re-running test cases that do not exercise changed or change-impacted parts of the program carries extra cost and gives no benefit. The research community has thus sought ways to optimize regression testing by lowering the cost of test re-execution while preserving its effectiveness. To this end, researchers have proposed selecting a subset of test cases according to a variety of criteria (test case selection) and reordering test cases for execution to maximize a score function (test case prioritization). This dissertation presents a novel framework for optimizing regression testing activities, based on a probabilistic view of regression testing. The proposed framework is built around predicting the probability that each test case finds faults in the regression testing phase, and optimizing the test suites accordingly. To predict such probabilities, we model regression testing using a Bayesian Network (BN), a powerful probabilistic tool for modeling uncertainty in systems. We build this model using information measured directly from the software system. Our proposed framework builds upon the existing research in this area in many ways. First, our framework incorporates different information extracted from software into one model, which helps reduce uncertainty by using more of the available information, and enables better modeling of the system. Moreover, our framework provides flexibility by enabling a choice of which sources of information to use. Research in software measurement has proven that dealing with different systems requires different techniques and hence requires such flexibility. Using the proposed framework, engineers can customize their regression testing techniques to fit the characteristics of their systems using measurements most appropriate to their environment. We evaluate the performance of our proposed BN-based framework empirically. Although the framework can help both test case selection and prioritization, we propose using it primarily as a prioritization technique. We therefore compare our technique against other prioritization techniques from the literature. Our empirical evaluation examines a variety of objects and fault types. The results show that the proposed framework can outperform other techniques on some cases and performs comparably on the others. In sum, this thesis introduces a novel Bayesian framework for optimizing regression testing and shows that the proposed framework can help testers improve the cost effectiveness of their regression testing tasks.

Developing a large software system involves many complicated, varied, and inter-dependent tasks, and these tasks are typically implemented using a combination of defined processes, semi-automated tools, and ad hoc practices. Stakeholders in the development process --- including software developers, managers, and customers --- often want to be able to track the actual practices being employed within a project. For example, a customer may wish to be sure that the process is ISO 9000 compliant, a manager may wish to track the amount of testing that has been done in the current iteration, and a developer may wish to determine who has recently been working on a subsystem that has had several major bugs appear in it. However, extracting the software development processes from an existing project is expensive if one must rely upon manual inspection of artifacts and interviews of developers and their managers. Previously, researchers have suggested the live observation and instrumentation of a project to allow for more measurement, but this is costly, invasive, and also requires a live running project. In this work, we propose an approach that we call software process recovery that is based on after-the-fact analysis of various kinds of software development artifacts. We use a variety of supervised and unsupervised techniques from machine learning, topic analysis, natural language processing, and statistics on software repositories such as version control systems, bug trackers, and mailing list archives. We show how we can combine all of these methods to recover process signals that we map back to software development processes such as the Unified Process. The Unified Process has been visualized using a time-line view that shows effort per parallel discipline occurring across time. This visualization is called the Unified Process diagram. We use this diagram as inspiration to produce Recovered Unified Process Views (RUPV) that are a concrete version of this theoretical Unified Process diagram. We then validate these methods using case studies of multiple open source software systems.

Software evolution refers to continuous change that a software system endures from inception to retirement. Each change must be efficiently and tractably propagated across models representing the system at different levels of abstraction. Model synchronization activities needed to support the systematic specification and analysis of evolution activities are still not adequately identified and formally defined. In our research, we first introduce a formal notation for the representation of domain models and model instances to form the theoretical basis for the proposed model synchronization framework. Besides conforming to a generic MOF metamodel, we consider that each software model also relates to an application domain context (e.g., operating systems, web services). Therefore, we are addressing the problems of model synchronization by focusing on domain-specific contexts. Secondly, we identify and formally define model dependencies that are needed to trace and propagate changes across system models at different levels of abstraction, such as from design to source code. The approach for extraction of these dependencies is based on Formal Concept Analysis (FCA) algorithms. We further model identified dependencies using Unified Modeling Language (UML) profiles and constraints, and utilize the extracted dependency relations in the context of coarse-grained model synchronization. Thirdly, we introduce modeling semantics that allow for more complex profile-based dependencies using Triple Graph Grammar (TGG) rules with corresponding Object Constraint Language (OCL) constraints. The TGG semantics provide for fine-grained model synchronization, and enable compliance with the Query/View/Transformation (QVT) standards. The introduced framework is assessed on a large, industrial case study of the IBM Commerce system. The dependency extraction framework is applied to repositories of business process models and related source code. The extracted dependencies were evaluated by IBM developers, and the corresponding precision and recall values calculated with results that match the scope and goals of the research. The grammar-based model synchronization and dependency modelling using profiles has also been applied to the IBM Commerce system, and evaluated by the developers and architects involved in development of the system. The results of this experiment have been found to be valuable by stakeholders, and a patent codifying the results has been filed by the IBM organization and has been granted. Finally, the results of this experiment have been formalized as TGG rules, and used in the context of fine-grained model synchronization.

Developing a large software system involves many complicated, varied, and inter-dependent tasks, and these tasks are typically implemented using a combination of defined processes, semi-automated tools, and ad hoc practices. Stakeholders in the development process --- including software developers, managers, and customers --- often want to be able to track the actual practices being employed within a project. For example, a customer may wish to be sure that the process is ISO 9000 compliant, a manager may wish to track the amount of testing that has been done in the current iteration, and a developer may wish to determine who has recently been working on a subsystem that has had several major bugs appear in it. However, extracting the software development processes from an existing project is expensive if one must rely upon manual inspection of artifacts and interviews of developers and their managers. Previously, researchers have suggested the live observation and instrumentation of a project to allow for more measurement, but this is costly, invasive, and also requires a live running project. In this work, we propose an approach that we call software process recovery that is based on after-the-fact analysis of various kinds of software development artifacts. We use a variety of supervised and unsupervised techniques from machine learning, topic analysis, natural language processing, and statistics on software repositories such as version control systems, bug trackers, and mailing list archives. We show how we can combine all of these methods to recover process signals that we map back to software development processes such as the Unified Process. The Unified Process has been visualized using a time-line view that shows effort per parallel discipline occurring across time. This visualization is called the Unified Process diagram. We use this diagram as inspiration to produce Recovered Unified Process Views (RUPV) that are a concrete version of this theoretical Unified Process diagram. We then validate these methods using case studies of multiple open source software systems.

Software engineering is a fundamentally collaborative activity, yet most tools that support software engineers are designed only for single users. There are many foreseen benefits in using tools that support real time collaboration between software engineers, such as avoiding conflicting concurrent changes to source files and determining the impact of program changes immediately. Unfortunately, it is difficult to develop non-trivial tools that support real time Collaborative Software Engineering (CSE). Accordingly, the few CSE tools that do exist have restricted capabilities. Given the availability of powerful desktop workstations and recent advances in distributed computing technology, it is now possible to approach the challenges of CSE from a new perspective. The research goal in this thesis is to investigate mechanisms for supporting real time CSE, and to determine the potential gains for developers from the use of CSE tools. An infrastructure, CAISE, is presented which supports the rapid development of real time CSE tools that were previously unobtainable, based on patterns of collaboration evident within software engineering. In this thesis, I discuss important design aspects of CSE tools, including the identification of candidate patterns of collaboration. I describe the CAISE approach to supporting small teams of collaborating software engineers. This is by way of a shared semantic model of software, protocol for tool communication, and Computer Supported Collaborative Work (CSCW) facilities. I then introduce new types of synchronous semantic model-based tools that support various patterns of CSE. Finally, I present empirical and heuristic evaluations of typical development scenarios. Given the CAISE infrastructure, it is envisaged that new aspects of collaborative work within software engineering can be explored, allowing the perceived benefits of CSE to be fully realised.

One of the factors that seems to influence an individual’s effectiveness in requirements engineering activities is his or her knowledge of the problem being solved, i.e., domain knowledge. While in-depth domain knowledge enables a requirements engineer to understand the problem easier, he or she can fall for tacit assumptions of the domain and might overlook issues that are obvious to domain experts and thus remain unmentioned. The purpose of this thesis is to investigate the impact of domain knowledge on different requirements engineering activities. The main research question this thesis attempts to answer is “How does one form the most effective team, consisting of some mix of domain ignorants and domain awares, for a requirements engineering activity involving knowledge about the domain of the computer-based system whose requirements are being determined by the team?” This thesis presents two controlled experiments and an industrial case study to test a number of hypotheses. The main hypothesis states that a requirements engineering team for a computer-based system in a particular domain, consisting of a mix of requirements analysts that are ignorant of the domain and requirements analysts that are aware of the domain, is more effective at requirement idea generation than a team consisting of only requirements analysts that are aware of the domain. The results of the controlled experiments, although not conclusive, provided some support for the positive effect of the mix on effectiveness of a requirements engineering team. The results also showed a significant effect of other independent variables, especially educational background. The data of the case study corroborated the results of the controlled experiments. The main conclusion that can be drawn from the findings of this thesis is that the presence in a requirements engineering team of a domain ignorant with a computer science or software engineering background improves the effectiveness of the team.

Software evolution refers to continuous change that a software system endures from inception to retirement. Each change must be efficiently and tractably propagated across models representing the system at different levels of abstraction. Model synchronization activities needed to support the systematic specification and analysis of evolution activities are still not adequately identified and formally defined. In our research, we first introduce a formal notation for the representation of domain models and model instances to form the theoretical basis for the proposed model synchronization framework. Besides conforming to a generic MOF metamodel, we consider that each software model also relates to an application domain context (e.g., operating systems, web services). Therefore, we are addressing the problems of model synchronization by focusing on domain-specific contexts. Secondly, we identify and formally define model dependencies that are needed to trace and propagate changes across system models at different levels of abstraction, such as from design to source code. The approach for extraction of these dependencies is based on Formal Concept Analysis (FCA) algorithms. We further model identified dependencies using Unified Modeling Language (UML) profiles and constraints, and utilize the extracted dependency relations in the context of coarse-grained model synchronization. Thirdly, we introduce modeling semantics that allow for more complex profile-based dependencies using Triple Graph Grammar (TGG) rules with corresponding Object Constraint Language (OCL) constraints. The TGG semantics provide for fine-grained model synchronization, and enable compliance with the Query/View/Transformation (QVT) standards. The introduced framework is assessed on a large, industrial case study of the IBM Commerce system. The dependency extraction framework is applied to repositories of business process models and related source code. The extracted dependencies were evaluated by IBM developers, and the corresponding precision and recall values calculated with results that match the scope and goals of the research. The grammar-based model synchronization and dependency modelling using profiles has also been applied to the IBM Commerce system, and evaluated by the developers and architects involved in development of the system. The results of this experiment have been found to be valuable by stakeholders, and a patent codifying the results has been filed by the IBM organization and has been granted. Finally, the results of this experiment have been formalized as TGG rules, and used in the context of fine-grained model synchronization.

Thesis (M.B.A.)--University of Hong Kong, 1991.

As formigas são criaturas fascinantes que, além dos avanços na biologia também inspiraram pesquisas sobre teoria da informação. Em particular, o estudo resultou na criação da Teoria da Forragem de Informação, que descreve como os agentes de buscam informações em seu ambiente. Esta teoria também explica fenômenos recentes e bem-sucedidos, como crowd sourcing. Crowdsourcing tem sido aplicado a muitas atividades em engenharia de software, incluindo desenvolvimento, tradução e testes, mas uma atividade parece resistir: depuração. No entanto, os desenvolvedores sabem que a depuração pode exigir dedicação, esforço, longas horas de trabalho, por vezes, para mudar uma linha de código único. Nós introduzimos o conceito de Depuração em Enxame, para trazer crowd sourcing para a atividade de depuração. Através de crowd sourcing, pretendemos ajudar os desenvolvedores, capitalizando a sua dedicação, esforço e longas horas de trabalho para facilitar atividades de depuração. Mostramos que a depuração enxame requer uma abordagem específica para recolher informações relevantes, e descrevemos sua infra-estrutura. Mostramos também que a depuração em enxame pode reduzir o esforço desenvolvedores. Concluímos com as vantagens e limitações atuais de depuração enxame, e sugerir caminhos para superar estas limitações e ainda mais a adoção de crowd sourcing para atividades de depuração. / Ants are fascinating creatures that beyond the advances in biology have also inspired research on information theory. In particular, their study resulted in the creation of the Information Foraging Theory, which describes how agents forages for information in their environment. This theory also explains recent and fruitful phenomena, such as crowdsourcing. Many activities in software engineering have applied crowdsourcing, including development, translation, and testing, but one action seems to resist: debugging. Developers know that debugging can require dedication, effort, long hours of work, sometimes for changing one line of code only. We introduce the concept of Swarm Debugging, to bring crowdsourcing to the activity of debugging. Through crowdsourcing, we aim at helping developers by capitalizing on their dedication, effort, and long hours of work to ease debugging activities of their peers or theirs, on other bugs. We show that swarm debugging requires a particular approach to collect relevant information, and we describe the Swarm Debugging Infrastructure. We also show that swarm debugging minimizes developers effort. We conclude with the advantages and current limitations of swarm debugging and suggest directions to overcome these limitations and further the adoption of crowdsourcing for debugging activities.

As formigas são criaturas fascinantes que, além dos avanços na biologia também inspiraram pesquisas sobre teoria da informação. Em particular, o estudo resultou na criação da Teoria da Forragem de Informação, que descreve como os agentes de buscam informações em seu ambiente. Esta teoria também explica fenômenos recentes e bem-sucedidos, como crowd sourcing. Crowdsourcing tem sido aplicado a muitas atividades em engenharia de software, incluindo desenvolvimento, tradução e testes, mas uma atividade parece resistir: depuração. No entanto, os desenvolvedores sabem que a depuração pode exigir dedicação, esforço, longas horas de trabalho, por vezes, para mudar uma linha de código único. Nós introduzimos o conceito de Depuração em Enxame, para trazer crowd sourcing para a atividade de depuração. Através de crowd sourcing, pretendemos ajudar os desenvolvedores, capitalizando a sua dedicação, esforço e longas horas de trabalho para facilitar atividades de depuração. Mostramos que a depuração enxame requer uma abordagem específica para recolher informações relevantes, e descrevemos sua infra-estrutura. Mostramos também que a depuração em enxame pode reduzir o esforço desenvolvedores. Concluímos com as vantagens e limitações atuais de depuração enxame, e sugerir caminhos para superar estas limitações e ainda mais a adoção de crowd sourcing para atividades de depuração. / Ants are fascinating creatures that beyond the advances in biology have also inspired research on information theory. In particular, their study resulted in the creation of the Information Foraging Theory, which describes how agents forages for information in their environment. This theory also explains recent and fruitful phenomena, such as crowdsourcing. Many activities in software engineering have applied crowdsourcing, including development, translation, and testing, but one action seems to resist: debugging. Developers know that debugging can require dedication, effort, long hours of work, sometimes for changing one line of code only. We introduce the concept of Swarm Debugging, to bring crowdsourcing to the activity of debugging. Through crowdsourcing, we aim at helping developers by capitalizing on their dedication, effort, and long hours of work to ease debugging activities of their peers or theirs, on other bugs. We show that swarm debugging requires a particular approach to collect relevant information, and we describe the Swarm Debugging Infrastructure. We also show that swarm debugging minimizes developers effort. We conclude with the advantages and current limitations of swarm debugging and suggest directions to overcome these limitations and further the adoption of crowdsourcing for debugging activities.