How to identify class comment types? A multi-language approach for class comment classification

BackgroundQualitative research methods are increasingly being used across disciplines because of their ability to help investigators understand the perspectives of participants in their own words. However, qualitative analysis is a laborious and resource-intensive process. To achieve depth, researchers are limited to smaller sample sizes when analyzing text data. One potential method to address this concern is natural language processing (NLP). Qualitative text analysis involves researchers reading data, assigning code labels, and iteratively developing findings; NLP has the potential to automate part of this process. Unfortunately, little methodological research has been done to compare automatic coding using NLP techniques and qualitative coding, which is critical to establish the viability of NLP as a useful, rigorous analysis procedure.ObjectiveThe purpose of this study was to compare the utility of a traditional qualitative text analysis, an NLP analysis, and an augmented approach that combines qualitative and NLP methods.MethodsWe conducted a 2-arm cross-over experiment to compare qualitative and NLP approaches to analyze data generated through 2 text (short message service) message survey questions, one about prescription drugs and the other about police interactions, sent to youth aged 14-24 years. We randomly assigned a question to each of the 2 experienced qualitative analysis teams for independent coding and analysis before receiving NLP results. A third team separately conducted NLP analysis of the same 2 questions. We examined the results of our analyses to compare (1) the similarity of findings derived, (2) the quality of inferences generated, and (3) the time spent in analysis.ResultsThe qualitative-only analysis for the drug question (n=58) yielded 4 major findings, whereas the NLP analysis yielded 3 findings that missed contextual elements. The qualitative and NLP-augmented analysis was the most comprehensive. For the police question (n=68), the qualitative-only analysis yielded 4 primary findings and the NLP-only analysis yielded 4 slightly different findings. Again, the augmented qualitative and NLP analysis was the most comprehensive and produced the highest quality inferences, increasing our depth of understanding (ie, details and frequencies). In terms of time, the NLP-only approach was quicker than the qualitative-only approach for the drug (120 vs 270 minutes) and police (40 vs 270 minutes) questions. An approach beginning with qualitative analysis followed by qualitative- or NLP-augmented analysis took longer time than that beginning with NLP for both drug (450 vs 240 minutes) and police (390 vs 220 minutes) questions.ConclusionsNLP provides both a foundation to code qualitatively more quickly and a method to validate qualitative findings. NLP methods were able to identify major themes found with traditional qualitative analysis but were not useful in identifying nuances. Traditional qualitative text analysis added important details and context.

Program comprehension is a required activity for many software development and maintenance tasks. A common means of understanding software is though UML diagrams. UML diagrams model the design and implementation of an application, describing both its structure and behaviour. However with rapid software development life cycles, UML diagrams often become inconsistent with the implementation of an application. This limits their effectiveness in program comprehension activities. This paper presents a program analysis framework to reverse engineer sequence diagrams from application source code. Part of this framework is the ability to filter out irrelevant operations from a sequence diagram in order to simplify the representation of an application. This is achieved by identifying important data, and following its use in the application. The purpose of this framework is to aid in program comprehension activities by providing up to date representations of an application; while also enabling developers to identify the logical operation of a program without interference from irrelevant or supporting operations.

Smart assistants are a technology that has become very popular today, due to the multiple functions they have, and somehow allow a natural interaction between devices and human beings. The objective is to develop an assistant based on NLP (Natural Language Processing) focused on the Java programming language, with the feature of guiding the user on the use of this language from reliable and validated sources, through speech recognition and speech synthesis by the assistant, allowing communication between the software and the user through natural language, in order to make the time in the consultation of information more efficient since it is considered that from the implementation of the speech to text engine and the subsequent processing of natural language, The search will be carried out on the web obtaining relevant results of precision. In the implemented methodology, the general requirements were defined, such as the voice recognition and identification modules, the search module and the internal storage structure, the design was based on a file-based controller view architecture, implemented through technologies such as PyQt5, Speech Recognition, Pyttsx3, Beautiful Soap among others, developed with the python programming language with the paradigm of object-oriented programming obtaining satisfactory results of precision in the searches by concepts and syntax carried out

An empirical study of software maintenance tasks was carried out in a large Norwegian organization in the period 1992–93. More than one hundred randomly selected maintenance tasks were studied through interviews with the maintainers performing the tasks immediately before they started the tasks and immediately after they had completed the tasks. The collected data is used to develop distributions and to test 33 hypotheses about software maintenance. The findings from the study indicate, for example, that Application system documentation plays only a minor role as maintenance information source. The maintenance productivity, measured in LOC/effort, is predicted by the size of the task and type of change in the source code, but seems rather independent of language level, maintainer experience, application age and application size. There is an economy of scale for software maintenance tasks. The type of programming language does not correlate with proportion of unexpected maintenance problems, task complexity or task size. The proportion of corrective maintenance decreases with increasing age of the application.

This study is to test the technologies related to natural language processing (NLP) and text analysis to solve the problem of symptom analysis and symptomatic treatment for healthcare workers and patients after large-scale outbreaks of infectious diseases. Using the keyword extraction tool based on NLP technology combined with text analysis, the symptom description of the infected population obtained from the questionnaire survey was analyzed, and the efficacy of the symptomatic treatment drug was analyzed, and finally a pre-experimental system of online symptomatic treatment support drug selection system was produced. Through natural language processing (NLP) and text analysis of the symptoms of infected people, we found that high-frequency symptoms were mainly reflected in: nasal congestion, sore throat, fever and other high-frequency keywords, and through the analysis of alternative drug libraries, we also found that the drugs were mainly concentrated in: cold medicines, antipyretic drugs,and traditional Chinese medicines. In the early stage of infectious disease outbreak, it is difficult to use the existing drug library to make a prompt system under the premise of insufficient data collection, and a relatively complete symptomatic treatment support medication system can be formed after accumulating certain samples.

We introduce Tasaheel-v2, an automated tool specifically developed for Arabic Natural Language Processing (NLP)and textual analysis tasks. This work is an extension to the first version, Tasaheel-v1, comprised of traditional NLP tasksincluding stemming, segmentation, normalization, named entity recognition, and part of speech tagging. Furthermore, itincluded cutting-edge analytic methods, such as specific emotion, polarity, linguistics, and domain-specific word tagging. In thisnew innovative version, Tasaheel-v2, we introduce additional benefiting utilities designed to provide assistance for the Arabicresearch community. We specifically integrate another Arabic-specific POS tagger, a sentiment analyzer, and English-to-Arabictranslation functions. We leverage the utilities provided in Tasaheel to develop a machine-learning model designed to identifyArabic phishing emails and provide a thorough textual analysis to capture deceptive cues used to detect phishing linguisticpatterns. This tool contributes to the Arabic research domain by providing assistive NLP functions and textual analysis featuresall in one tool

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

Code comments are one of the effective means for assisting programmers to understand the source code. High-quality code comments play an important role in areas such as software maintenance and software reuse. Good code comments can help programmers understand the role of source code and facilitate comprehension of programs and software maintenance tasks quickly. But in reality, most programmers only pay attention to the code and ignore comments and documents, which greatly reduce the program's readability and maintainability. This paper has compiled the relevant research on code comments so far, mainly including four aspects: automatic generation of code comments, consistency of code comments, classification of code comments, and quality evaluation of code comments. By analyzing relevant methods in this research field, it provides more complete information for future research.

Bug reports are widely employed to facilitate software tasks in software maintenance. Since bug reports are contributed by people, the authorship characteristics of contributors may heavily impact the performance of resolving software tasks. Poorly written bug reports may delay developers when fixing bugs. However, no in-depth investigation has been conducted over the authorship characteristics. In this study, we first leverage byte-level $N$-grams to model the authorship characteristics and employ Normalized Simplified Profile Intersection (NSPI) to identify the similarity of the authorship characteristics. Then, we investigate a series of properties related to contributors authorship characteristics, including the evolvement over time and the variation among distinct products in open source projects. Moreover, we show how to leverage the authorship characteristics to facilitate a well-known task in software maintenance, namely Bug Report Summarization (BRS). Experiments on open source projects validate that incorporating the authorship characteristics can effectively improve a state-of-the-art method in BRS. Our findings suggest that contributors should retain stable authorship characteristics and the authorship characteristics can assist in resolving software tasks.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

MathWorks provides a platform-independent execution engine that runs MATLAB code, a single desktop environment to develop the code and an extensive library of well-documented functions to assist one in solving programming problems. In deciding to work with MATLAB, the user is choosing a tightly bundled package of tools, optimized and improved over decades. However, other programming languages and software can offer additional functionality and power. This chapter discusses the integration of software written in other programming languages and environments. Examples are shown for system commands, the Java and Python programming languages and the .NET framework.

Software maintainers have long been acutely aware of the challenges involved in managing software change processes. Activities such as software migration, restructuring and reengineering all involve source code modification. They rely heavily on analysis and comprehension of the complex system structures and interactions that characterize both legacy and modern software systems. It is widely accepted that tools that support software analysis and maintenance tasks would go a long way towards addressing the constraints that software developers and maintainers work with on a day-to-day basis. Nevertheless, despite an abundance of tools, not many are used by practitioners and very few are considered essential for a particular development or maintenance task. This workshop examined design issues related to the development and application of tools to support software analysis and maintenance

During software development and maintenance, developers spend a considerable amount of time on program comprehension activities. Previous studies show that program comprehension takes up as much as half of a developer’s time. However, most of these studies are performed in a controlled setting, or with a small number of participants, and investigate the program comprehension activities only within the IDEs. However, developers’ program comprehension activities go well beyond their IDE interactions. In this paper, we extend our ActivitySpace framework to collect and analyze Human-Computer Interaction (HCI) data across many applications (not just the IDEs). We follow Minelli et al.’s approach to assign developers’ activities into four categories: navigation, editing, comprehension, and other. We then measure the comprehension time by calculating the time that developers spend on program comprehension, e.g., inspecting console and breakpoints in IDE, or reading and understanding tutorials in web browsers. Using this approach, we can perform a more realistic investigation of program comprehension activities, through a field study of program comprehension in practice across a total of seven real projects, on 78 professional developers, and amounting to 3,148 working hours. Our study leverages interaction data that is collected across many applications by the developers. Our study finds that on average developers spend $\sim$ 58 percent of their time on program comprehension activities, and that they frequently use web browsers and document editors to perform program comprehension activities. We also investigate the impact of programming language, developers’ experience, and project phase on the time that is spent on program comprehension, and we find senior developers spend significantly less percentages of time on program comprehension than junior developers. Our study also highlights the importance of several research directions needed to reduce program comprehension time, e.g., building automatic detection and improvement of low quality code and documentation, construction of software-engineering-specific search engines, designing better IDEs that help developers navigate code and browse information more efficiently, etc.

Objective - This paper focuses on analyzing the significance of sports footwear brands by processing large text data from the Internet. In a modern environment, the brand distinguishes a company's products or services from those of its competitors. A strong brand can help build trust with customers as they perceive the brand as reliable and trustworthy. Methodology/Technique - The study uses NLP (Natural Language Processing) methods to analyze rich text content on the Internet. The research focus is based on the application of innovative methods to determine the importance and value of a brand using NLP techniques by analyzing the content of a large corpus of text originating from websites dealing with sports footwear brands. The NLP analysis models were programmed using the low-code analysis tool KNIME. Findings – The analysis is carried out for the most well-known sports footwear brands such as Nike, Adidas, Puma, Under Armour, Reebok and Asics. The research object refers to the analysis of brand significance and the evaluation of consumer opinions on sports footwear, based on the processing of large text data from the internet. Novelty - The research results are based on an innovative approach to measuring and evaluating the brand significance in sports footwear using NLP methods to analyze large text content from the Internet. The results obtained show that this new approach to metrics and evaluation can significantly improve existing methods of brand evaluation. Type of Paper: Empirical JEL Classification: M32, M39. Keywords: Brand, NLP Method, Text Analysis, Online Brand Management Strategies, Sports Footwear. Reference to this paper should be made as follows: Tarade, S.S; Vuković, D. (2023). Discovering the Significance of Sports Footwear Brands through Text Analysis, J. Mgt. Mkt. Review, 8(4), 166 – 175. https://doi.org/10.35609/jmmr.2023.8.4(7)

Bots can be broadly divided into two categories: chat bots and smart bots. Chat bots respond using a predefined set of rules and hence the responses are limited. For example, if you build a bot for reserving a table in a restaurant, the bot would always ask the basic questions of date and time, number of people, and seating preference (indoor/outdoor). If you throw a random request at the chat bot, it might not respond with a meaningful message or might just respond with a generic message. Smart bots are more intelligent. They work with a wide variety of information and generate more human-like responses. Smart bots are designed to learn from the conversation and provide more-useful answers as the conversation progresses, leaving the impression of a human-to-human conversation. Bots can be designed to create smarter responses by using cutting-edge artificial intelligence algorithms. Authoring AI algorithms is a complex task, requiring a varied skill set and lots of analysis to build an AI algorithm that can perform tasks like natural language processing and sentiment analysis or generate recommendations. Microsoft Cognitive Services provides a basket of AI algorithms that can be integrated into any application. These algorithms were developed by an expert team and cater across the fields of computer vision, speech, text analysis, natural language processing, knowledge extraction, and web search. In this chapter, we will learn to build smarter bots using Microsoft Cognitive Services and come to understand the capabilities of the evolving list of powerful AI algorithms.