Software maintenance by program transformation in a wide spectrum language

This thesis addresses the software maintenance problem of extracting high-level designs from code. The investigated solution is to use a mathematically-based formal program transformation system. The resulting tool, the Maintainer's Assistant, is based on Ward's [177] WSL (wide spectrum language) and method of proving program equivalence. The problems addressed include: how to reverse engineer from code alone (the only reliable source of information about a program [158]), how to express program transformations within the system, what kinds of transformations should be incorporated, how to make the tool simple to use, how to perform abstraction and how to create a tool suitable for use with large programs. Using the Maintainer's Assistant, the program code is automatically translated into WSL and the transformations, although tested for valid applicability by the system, are interactively applied by the user. Notable features include a mathematical simplifier, a large flexible transformation catalogue and, significantly, the use of an extension of WSL, A4etaWSL, for representing the transformations. MetaWSL expands WSL by incorporating a variety of extensions, including: program editing statements, pattern matching and template filling functions, symbolic mathematics and logic functions, statements for moving within the program’s syntax tree and statements for repeating an operation at each node of the tree. Using MetaWSL, 80% of the 601 transformations can be expressed in less than 20 program statements. The Maintainer's Assistant has been used on a wide variety of examples of up to several thousand lines, including commercial software written in IBM 370 assembler. It has been possible to transform initially unstructured programs into a hierarchy of procedures, facilitating subsequent design recovery. These results show that program transformation is a viable method of renovating old (370 assembler) code in a cost elective way, and that MetaWSL provides an effective basis for clearly and concisely expressing the required transformations

Automatically learning optimal formula simplifiers and database entity matching rules

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 153-161).Traditionally, machine learning (ML) is used to find a function from data to optimize a numerical score. On the other hand, synthesis is traditionally used to find a function (or a program) that can be derived from a grammar and satisfies a logical specification. The boundary between ML and synthesis has been blurred by some recent work [56,90]. However, this interaction between ML and synthesis has not been fully explored. In this thesis, we focus on the problem of finding a function given large amounts of data such that the function satisfies a logical specification and also optimizes a numerical score over the input data. We present a framework to solve this problem in two impactful application domains: formula simplification in constraint solvers and database entity matching (EM). First, we present a system called Swapper based on our framework that can automatically generate code for efficient formula simplifiers specialized to a class of problems. Formula simplification is an important part of modern constraint solvers, and writing efficient simplifiers has largely been an arduous manual task. Evaluation of Swapper on multiple applications of the Sketch constraint solver showed 15-60% improvement over the existing hand-crafted simplifier in Sketch. Second, we present a system called EM-Synth based on our framework that generates as effective and more interpretable EM rules than the state-of-the-art techniques. Database entity matching is a critical part of data integration and cleaning, and it usually involves learning rules or classifiers from labeled examples. Evaluation of EM-Synth on multiple real-world datasets against other interpretable (shallow decision trees, SIFI [116]) and noninterpretable (SVM, deep decision trees) methods showed that EM-Synth generates more concise and interpretable rules without sacrificing too much accuracy.by Rohit Singh.Ph. D

Ontology evolution in physics

With the advent of reasoning problems in dynamic environments, there is an increasing need for automated reasoning systems to automatically adapt to unexpected changes in representations. In particular, the automation of the evolution of their ontologies needs to be enhanced without substantially sacrificing expressivity in the underlying representation. Revision of beliefs is not enough, as adding to or removing from beliefs does not change the underlying formal language. General reasoning systems employed in such environments should also address situations in which the language for representing knowledge is not shared among the involved entities, e.g., the ontologies in a multi-ontology environment or the agents in a multi-agent environment. Our techniques involve diagnosis of faults in existing, possibly heterogeneous, ontologies and then resolution of these faults by manipulating the signature and/or the axioms. This thesis describes the design, development and evaluation of GALILEO (Guided Analysis of Logical Inconsistencies Lead to Evolution of Ontologies), a system designed to detect conflicts in highly expressive ontologies and resolve the detected conflicts by performing appropriate repair operations. The integrated mechanism that handles ontology evolution is able to distinguish between various types of conflicts, each corresponding to a unique kind of ontological fault. We apply and develop our techniques in the domain of Physics. This an excellent domain because many of its seminal advances can be seen as examples of ontology evolution, i.e. changing the way that physicists perceive the world, and case studies are well documented – unlike many other domains. Our research covers analysing a wide ranging development set of case studies and evaluating the performance of the system on a test set. Because the formal representations of most of the case studies are non-trivial and the underlying logic has a high degree of expressivity, we face some tricky technical challenges, including dealing with the potentially large number of choices in diagnosis and repair. In order to enhance the practicality and the manageability of the ontology evolution process, GALILEO incorporates the functionality of generating physically meaningful diagnoses and repairs and, as a result, narrowing the search space to a manageable size

A User-aware Intelligent Refactoring for Discrete and Continuous Software Integration

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. Furthermore, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. In addition, it is challenging to merge these refactorings with other changes performed in parallel by developers. In this thesis, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses Non-dominated Sorting Genetic Algorithm (NSGAII) to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. We combined our interactive approach and unsupervised learning to reduce the developer’s interaction effort when refactoring a system. The unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. To reduce the interaction effort, we propose an approach to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on their preferences. Since developers may want to focus on specific code locations, the ”Decision Space” is also important. Therefore, our interactive approach enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Due to an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Our bot continuously monitors the software repository and find the best sequence of refactorings to fix the quality issues in Continous Integration/Continous Development (CI/CD) environments as a set of pull-requests generated after mining previous code changes to understand the profile of developers. We quantitatively and qualitatively evaluated the performance and effectiveness of our proposed approaches via a set of studies conducted with experienced developers who used our tools on both open source and industry projects.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154775/1/Vahid Alizadeh Final Dissertation.pdfDescription of Vahid Alizadeh Final Dissertation.pdf : Dissertatio

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio