Hawaii International Conference on System Sciences

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

斜向妥当化(Cross-Validation)の必要と方法

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Automated Evaluation of Text and Discourse with Coh-Metrix: Introduction

Spreadsheets are used extensively in business processes around the world and just like software, spreadsheets are changed throughout their lifetime causing maintainability issues. This paper adapts known code smells to spreadsheet formulas. To that end we present a list of metrics by which we can detect smelly formulas and a visualization technique to highlight these formulas in spreadsheets. We implemented the metrics and visualization technique in a prototype tool to evaluate our approach in two ways. Firstly, we analyze the Euses spreadsheet corpus, to study the occurrence of the formula smells. Secondly, we analyze ten real life spreadsheets, and interview the spreadsheet owners about the identified smells. The results of these evaluations indicate that formula smells are common and that they can reveal real errors and weaknesses in spreadsheet formulas.

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Detecting Code Smells in Spreadsheet Formulas

The need to explicitly document design decisions has been emphasized both in research and in industry. To address design concerns, software architects and developers implicitly capture design decisions in tools such as issue management systems. These design decisions are not explicitly labeled and are not integrated with the architecture knowledge management tools. Automatically extracting design decisions will aid architectural knowledge management tools to learn from the past decisions and to guide architects while making decisions in similar context. In this paper, we propose a two-phase supervised machine learning based approach to first, automatically detect design decisions from issues and second, to automatically classify the identified design decisions into different decision categories. We have manually analyzed and labeled more than 1,500 issues from two large open source repositories and have used this dataset for generating the machine learning models. We have made the dataset publicly available that will serve as a starting point for researchers to further reference and investigate the design decision detection and classification problem. Our evaluation shows that by using linear support vector machines, we can detect design decisions with 91.29% accuracy and classify them with an accuracy of 82.79%. This provides a quantitative basis for learning from past design decisions to support stakeholders in making better and informed design decisions.

Automatic Extraction of Design Decisions from Issue Management Systems: A Machine Learning Based Approach

Literature review studies are essential and form the foundation for any type of research. They serve as the point of departure for those seeking to understand a research topic, as well as, helps research communities to reflect on the ideas, fundamentals, and approaches that have emerged, been acknowledged, and formed the state-of-the-art. In this paper, we present a semi-systematic literature review of 218 papers published over the last four decades that have contributed to a better understanding of architectural design decisions (ADDs). These publications cover various related topics including tool support for managing ADDs, human aspects in architectural decision making (ADM), and group decision making. The results of this paper should be treated as a getting-started guide for researchers who are entering the investigation phase of research on ADM. In this paper, the readers will find a brief description of the contributions made by the established research community over the years. Based on those insights, we recommend our readers to explore the publications and the topics in depth.

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The Evolution of Architectural Decision Making as a Key Focus Area of Software Architecture Research: A Semi-Systematic Literature Study

Software architecture can be seen as a set of architectural design decisions. These decisions, especially their rationale, play an important role in understanding software systems and constraints that are imposed on future design decisions. Documenting and managing these design decisions takes a lot of effort and is therefore often neglected by software architects. Following our vision to avoid explicit architectural knowledge management, we present an innovative tool used in industrial practise that is able to automatically recover design decisions from natural language text. The identified design decisions are then used to automatically derive further architectural knowledge, including related quality attributes and architectural elements to inhibit architectural erosion. By automatically enriching architectural knowledge and providing recommendations, the tool offers effective support for software architects in the decision-making process. The knowledge base of the tool is automatically kept up to date with information from different systems used by architects during the software development lifecycle process.

ADeX: A Tool for Automatic Curation of Design Decision Knowledge for Architectural Decision Recommendations

The architecture of any software can be thought of as a blueprint of its structure. This blueprint is an artifact generated based on a series of decisions taken by software architects and determines the overall quality of the resulting software. The first part of this paper focuses on identifying and formalizing the decision-making models in the context of designing software. Three models are investigated in detail: the rational economic model, the bounded rational model, and the recognition-primed decision model. The steps of decision making are mapped to the OODA Loop (Observe, Orient, Decide and Act) decision cycle as a generic framework for decision making. The second part of this paper focuses on documenting cognitive biases in the context of architectural decision making. Architects, being human, are invariably subject to the influence of cognitive biases due to the cognitive limitations of the human mind, resulting in a systematic deviation from the ideal decision-making process. This leads to the design of sub-par solutions because of missing rationality behind the decisions. A two-level classification is made to modularize the extensive list of biases that influence the architectural decision-making process. As an important outcome of this research, detailed information about each bias is documented as part of a cognitive bias catalog.

Decision Making and Cognitive Biases in Designing Software Architectures

In large software engineering projects, designing software systems is a collaborative decision-making process where a group of architects and developers make design decisions on how to address design concerns by discussing alternative design solutions. For the decision-making process, involving appropriate individuals requires objectivity and awareness about their expertise. In this paper, we propose a novel expert recommendation system that identifies individuals who could be involved in tackling new design concerns in software engineering projects. The approach behind the proposed system addresses challenges such as identifying architectural skills, quantifying architectural expertise of architects and developers, and finally matching and recommending individuals with suitable expertise to discuss new design concerns. To validate our approach, a quantitative evaluation of the recommendation system was performed using design decisions from four software engineering projects. The evaluation not only indicates that individuals with architectural expertise can be identified for design decision making but also provides quantitative evidence for the existence of personal experience bias during the decision-making process.

Klym Shumaiev

Papers

Automatic Extraction of Design Decisions from Issue Management Systems: A Machine Learning Based Approach

The Evolution of Architectural Decision Making as a Key Focus Area of Software Architecture Research: A Semi-Systematic Literature Study

ADeX: A Tool for Automatic Curation of Design Decision Knowledge for Architectural Decision Recommendations

Decision Making and Cognitive Biases in Designing Software Architectures

An Expert Recommendation System for Design Decision Making: Who Should be Involved in Making a Design Decision?