Understanding the effects of requirements volatility in software engineering by using analytical modeling and software process simulation

This paper introduces an executable system dynamics simulation model developed to help project managers comprehend the complex impacts related to requirements volatility on a software development project. The simulator extends previous research and adds research results from an empirical survey, including over 50 new parameters derived from the associated survey data, to a base model. The paper discusses detailed results from two cases that show significant cost, schedule, and quality impacts as a result of requirements volatility. The simulator can be used as an effective tool to demonstrate the complex set of factor relationships and effects related to requirements volatility.     

Empirical software engineering: Industrial experience reports

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Empirical software engineering: Industrial experience reports     

Experience using cleanroom software engineering

The US Department of Defense spends the largest portion of its software budget on reengineering and maintenance. The project profiled in this article, the US Army's first attempt to improve these processes using cleanroom methods, resulted in greater productivity, improved job satisfaction, and a high return on investment     

Toward data mining engineering: A software engineering approach

The number, variety and complexity of projects involving data mining or knowledge discovery in databases activities have increased just lately at such a pace that aspects related to their development process need to be standardized for results to be integrated, reused and interchanged in the future. Data mining projects are quickly becoming engineering projects, and current standard processes, like CRISP-DM, need to be revisited to incorporate this engineering viewpoint. This is the central motivation of this paper that makes the point that experience gained about the software development process over almost 40 years could be reused and integrated to improve data mining processes. Consequently, this paper proposes to reuse ideas and concepts underlying the IEEE Std 1074 and ISO 12207 software engineering model processes to redefine and add to the CRISP-DM process and make it a data mining engineering standard.     

Object-oriented data modeling in rule-based software development environments

Object-oriented databases (OODBs) were developed to support advanced applications for which traditional databases are not sufficient. The data management requirements of these new applications are significantly different from more traditional data processing applications. More light needs to be shed on these requirements in order to identify the aspects of OODBs that can lead to standards. We have studied the data management requirements of one class of advanced database applications: rule-based software development environments (RBDEs). RBDEs store project components in an object database and control access to these objects through a rule-based process model, which defines each development activity as a rule. The components are abstracted as instances of classes which are defined by the project's data model. In this paper we discuss the constructs that a data modeling language for RBDEs should provide, and then explore some of the data management requirements of RBDEs. We use the Marvel system we developed at Columbia as an example.     

软件工程关联数据的自动构建

针对目前在分布异构的大规模软件开发中难以高效地知晓信息和发现知识的问题,将语义网引入软件工程领域,对多源异构数据进行细粒度语义关联,提出本体构建、关联抽取和发现的方法,实现基于本体的软件工程关联数据的自动构建。该方法对软件工程本体进行概念抽取、合并、实例消解和属性消歧,从软件仓库结构化数据集中抽取出完整无冗余的关联数据;并采用同义词、动宾短语和结构关系三个特征利用自然语言处理(NLP)技术和信息检索(IR)技术从软件仓库中发现潜在的关联数据。实验结果表明,所提出的方法能从分布式软件工程数据集中自动构建和融合生成软件工程本体,并有效地发现潜在的关联数据将其扩充到软件工程本体中;与Baseline、Phraing和O-CSTI三种方法相比,关联数据发现的召回率、精准率和F值都有显著提高。     

Course design using software engineering methods

This paper describes a pedagogical project that was conducted to design a course on Dedicated Microcomputer Design. Based on the similarities which exist between the design of a course and the structured development of a computer program, an adaptation of some software engineering methodologies was used to design this new course. All the design tasks which were performed to realize this project are presented here in the context of the seven design phases of a Course Life Cycle model.     

Knowledge and software modeling using UML

Ontology can be considered as a comprehensive knowledge model which enables the developer to practice knowledge, instead of code, reuse. In the development of knowledge-based systems, different modeling languages are employed at different stages of the development process. By using a common modeling language for the knowledge and software models, knowledge instead of software reuse can be achieved. We illustrate the process by first presenting an ontology developed for an industrial domain and then investigate Unified Modeling Language (UML) as an ontology modeling tool. Since any model expressed in UML can be translated into a software model, the transition from the knowledge model to system implementation is better supported with the proposed approach. The industrial domain of selecting a remediation technique for petroleum contaminated sites is adopted for the illustration case study.     

Complex petrogenetic modeling using spreadsheet software

Continuous melting and open-system fractional crystallization modeling can be performed readily using spreadsheet software. Separate portions of the spreadsheet are used for a distribution coefficient matrix, calculation of the bulk distribution coefficients, and a work area for the entry of data and the calculation of new compositions and mineral mass fractions. The relative ease of creating the models in the spreadsheet format, the ability to recalculate quickly the model when any of the variables are changed, and the graphics capabilities of the software, demonstrate the advantages of this approach as opposed to writing a computer program for the same task.     

Modeling uncertainty in software engineering using rough sets

Uncertainty casts a shadow over all facets of software engineering. This negative meta-property is found in every aspect of software including requirement specifications, design, and code. It can also manifest itself in the tools and engineering practices employed, and in the off-the-shelf software incorporated into the final product. Unfortunately, it is often the case that software engineers ignore these sources of uncertainty or abstract them away. Perhaps this is because there is insufficient understanding of this uncertainty, and no universal techniques for handling its many forms. This paper focuses on the issues of uncertainty in software engineering. It further describes a rough set framework for making decisions in the face of such uncertainty and inconsistency. In particular, we show how to induce rule-based decision making from uncertain information in software engineering applications. Moreover, a freely available tool, Rosetta, is employed to automate the decision-making process. NASA has mandated the use of commercial off-the-shelf (COTS) solutions where possible. But in commercial real-time operating systems certain attributes are uncertain, even where published information is available. Therefore, the selection of a commercial real-time operating system for an embedded system is the software engineering problem with which we explain the rough set decision-making process.     

Finding the right data for software cost modeling

Good software cost models can significantly help software project managers. With good models, project stakeholders can make informed decisions about how to manage resources, how to control and plan the project, or how to deliver the project on time, on schedule, and on budget. Real-world data sets, such as those coming from software engineering projects, often contain noisy, irrelevant, or redundant variables. We propose that cost modelers should perform data-pruning experiments after data collection and before model building. Such pruning experiments are simple and fast.     

Support for software engineering using MODULA-2

Continuing a series on modern high-level languages the paper describes the features of MODULA-2 which are important in industrial systems programming. These features include the creation of libraries of reusable software, the ability to perform low-level programming and support for concurrency. MODULA-2 supports good software engineering practice by providing facilities for program decomposition, separate compilation and data abstraction. The role of the module concept and the export/import mechanism in providing these features is examined. Program modules, the definition and implementation parts of library modules and local modules are described.     

Structuring the modeling space and supporting evolution in software product line engineering

The scale and complexity of product lines means that it is practically infeasible to develop a single model of the entire system, regardless of the languages or notations used. The dynamic nature of real-world systems means that product line models need to evolve continuously to meet new customer requirements and to reflect changes of product line artifacts. To address these challenges, product line engineers need to apply different strategies for structuring the modeling space to ease the creation and maintenance of models. This paper presents an approach that aims at reducing the maintenance effort by organizing product lines as a set of interrelated model fragments defining the variability of particular parts of the system. We provide support to semi-automatically merge fragments into complete product line models. We also provide support to automatically detect inconsistencies between product line artifacts and the models representing these artifacts after changes. Furthermore, our approach supports the co-evolution of models and their respective meta-models. We discuss strategies for structuring the modeling space and show the usefulness of our approach using real-world examples from our ongoing industry collaboration.     

Towards an ontology modeling tool. A validation in software engineering scenarios

Ontology creation and management related processes are very important to define and develop semantic services. Ontology Engineering is the research field that provides the mechanisms to manage the life cycle of the ontologies. However, the process of building ontologies can be tedious and sometimes exhaustive. OWL-VisMod is a tool designed for developing ontological engineering based on visual analytics conceptual modeling for OWL ontologies life cycle management, supporting both creation and understanding tasks. This paper is devoted to evaluate OWL-VisMod through a set of defined tasks. The same tasks also will be done with the most known tool in Ontology Engineering, Protégé, in order to compare the obtained results and be able to know how is OWL-VisMod perceived for the expert users. The comparison shows that both tools have similar acceptation scores, but OWL-VisMod presents better feelings regarding user’s perception tasks due to the visual analytics influence.     

A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data

This research examined the use of the International Software Benchmarking Standards Group (ISBSG) repository for estimating effort for software projects in an organization not involved in ISBSG. The study investigates two questions: (1) What are the differences in accuracy between ordinary least-squares (OLS) regression and Analogy-based estimation? (2) Is there a difference in accuracy between estimates derived from the multi-company ISBSG data and estimates derived from company-specific data? Regarding the first question, we found that OLS regression performed as well as Analogy-based estimation when using company-specific data for model building. Using multi-company data the OLS regression model provided significantly more accurate results than Analogy-based predictions. Addressing the second question, we found in general that models based on the company-specific data resulted in significantly more accurate estimates.     

Data storage and extraction in engineering software using XML

Most engineering analysis programs rely on customized procedures for storing and accessing scientific data. The size of the data set needed to characterize an engineering problem can be substantial, particularly as the problem increases in complexity. This paper presents a methodology for greatly improving the accessibility of scientific data using the Extensible Markup Language (XML) to define a consistent procedure for data storage and retrieval. When the data is expressed in an XML format, any portion of the data set can be easily accessed by interfacing with an XML parser. Examples are provided for automating design calculations and CAD drawings.     

大数据视域下软件工程技术应用的实现路径

随着大数据时代的来临,大数据技术深入贯穿社会生产、生活的方方面面,尤其是随着5G通信技术的广泛应用,软件工程技术的应用迎来了前所未有的发展机遇和挑战。文章将大数据视域下软件工程技术应用的实现路径作为研究对象,从大数据及软件工程技术概述入手,充分分析了软件工程的核心技术,进而提出大数据视域下软件工程技术应用的实现路径,希望以此来不断提高软件工程技术的应用水平。