Applications of Multi-view Learning Approaches for Software Comprehension

Program comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software system. Each of these views contain unique and complementary information; together which can more accurately describe the data. In this paper, we investigate various techniques for combining different sources of information to improve the performance of a program comprehension task. We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. The experiments conducted on 10 relatively large Java software systems show that by fusing knowledge from different views, we can guarantee a lower bound on the quality of the modularization and even improve upon it. We proceed by integrating different sources of information to give a set of high-level recommendations as to how to refactor the software system. Furthermore, we demonstrate how learning a joint subspace allows for performing cross-modal retrieval across views, yielding results that are more aligned with what the user intends by the query. The multi-view approaches outlined in this paper can be employed for addressing problems in software engineering that can be encoded in terms of a learning problem, such as software bug prediction and feature location

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry

Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning

The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics.Comment: TSAS Submissio

The 'physics of diagrams' : revealing the scientific basis of graphical representation design

Data is omnipresent in the modern, digital world and a significant number of people need to make sense of data as part of their everyday social and professional life. Therefore, together with the rise of data, the design of graphical representations has gained importance and attention. Yet, although a large body of procedural knowledge about effective visualization exists, the quality of representations is often reported to be poor, proposedly because these guidelines are scattered, unstructured and sometimes perceived as contradictive. Therefore, this paper describes a literature research addressing these problems. The research resulted in the collection and structuring of 81 guidelines and 34 underlying propositions, as well as in the derivation of 7 foundational principles about graphical representation design, called the "Physics of Diagrams", which are illustrated with concrete, practical examples throughout the paper

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Operationalized Intent for Improving Coordination in Human-Agent Teams

With the increasing capabilities of artificial intelligent agents (AIAs) integrated into multi-agent systems, future concepts include human-agent teams (HATs) in which the members perform fluidly as a coordinated team. Research on coordination mechanisms in HATs is largely focused on AIAs providing information to humans to coordinate better (i.e. coordination from the AIA to the human). We focus on the compliment where AIAs can understand the operator to better synchronize with the operator (i.e. from the human to the AIA). This research focuses specifically on AIA estimation of operator intent. We established the Operationalized Intent framework which captures intent in a manner relevant to operators and AIAs. The core of operationalized intent is a quality goal hierarchy and an execution constraint list. Designing a quality goal hierarchy entails understanding the domain, the operators, and the AIAs. By extending established cognitive systems engineering analyses we developed a method to define the quality goals and capture the situations that influence their prioritization. Through a synthesis of mental model evaluation techniques, we defined and executed a process for designing human studies of intent. This human-in-the-loop study produced a corpus of data which was demonstrated the feasibility of estimating operationalized intent

Productivity Effects of Information Diffusion in Networks

We examine the drivers of diffusion of information through organizations and the effects on performance. In particular, we ask: What predicts the likelihood of an individual becoming aware of a strategic piece of information, or becoming aware of it sooner? Do different types of information exhibit different diffusion patterns, and do different characteristics of social structure, relationships and individuals in turn affect access to different kinds of information? Does better access to information predict an individual’s ability to complete projects or generate revenue? We hypothesize that the dual effects of content and structure jointly predict the diffusion path of information, and ultimately performance. To test our hypotheses, we characterize the social network of a medium sized executive recruiting firm using accounting data on project co-work relationships and ten months of email traffic observed over two five month periods. We identify two distinct types of information diffusing over this network – ‘event news’ and ‘discussion topics’ – by their usage characteristics, and observe several thousand diffusion processes of each type of information from their original first use to their varied recipients over time. We then test the effects of network structure and functional and demographic characteristics of dyadic relationships on the likelihood of receiving each type of information and receiving it more quickly. Our results demonstrate that the diffusion of news, characterized by a spike in communication and rapid, pervasive diffusion through the organization, is influenced by demographic and network factors but not by functional relationships (e.g. prior co-work, authority) or the strength of ties. In contrast, diffusion of discussion topics, which exhibit more shallow diffusion characterized by ‘back-and-forth’ conversation, is heavily influenced by functional relationships and the strength of ties, as well as demographic and network factors. Discussion topics are more likely to diffuse vertically up and down the organizational hierarchy, across relationships with a prior working history, and across stronger ties, while news is more likely to diffuse laterally as well as vertically, and without regard to the strength or function of relationships. Furthermore, we find that access to information strongly predicts the number of projects completed by each individual and the amount of revenue that person generates. The effects are economically significant, with each additional “word seen” correlated with about $70 of additional revenue generated. Our findings highlight the importance of simultaneous considerations of structure and content in information diffusion studies and provide some of the first evidence on the economic importance of information diffusion in networks.The National Science Foundation, Cisco Systems, France Telecom and the MIT Center for Digital Busines

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

THE NAVY FITREP: THE DEVELOPMENT AND TESTING OF A PERFORMANCE EVALUATION PROTOTYPE

The U.S. Navy currently faces the challenge of redesigning the Navy performance evaluation tool to best assess sailors for promotion eligibility. Navy Personnel Command has partnered with a research team at Naval Postgraduate School to create a performance evaluation tool that utilizes a behavior-based performance method and reflects the organizational goals of the Navy. This thesis integrates academic literature to support the development of a performance evaluation prototype. It examines the prototype’s ability across numerous demographics to assess a sailor’s current performance, their potential performance at the next paygrade, and their performance compared to their peers, for the purpose of promotion. The prototype was fielded by employing two recruitment methods for identifying and recruiting participants: snowballing and a social media campaign. This thesis investigates the utilization of behaviorally anchored rating scales for the purpose of performance evaluation, the demographic make-up of our sample, and the distributions of ratings within the three areas of assessment: performance, potential, and peer comparison. Based on the data, I recommend future research be conducted to evaluate the efficacy of performance appraisal ratings feeding directly into RSCA, in counteracting rater subjectivity and complacency. Subsequent testing should then be fielded to assess rater subjectivity and potential adverse impacts on the efficacy of the prototype.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Lieutenant, United States Navy ReserveApproved for public release. Distribution is unlimited

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.