ERP requirements engineering practice: lessons learned

Standard off-the-shelf requirements engineering processes have become a key to conceptualizing any integrated, corporate-wide solution based on packaged enterprise resource planning software. A generic RE model offers defined processes, suggests process stakeholders, specifies steps to accomplish tasks, indicates task dependencies, and provides standard tool support for ERP RE. Essentially, any off-the-shelf RE process is about composition and reconciliation: you start with a general set of business process and data requirements, then explore standard ERP functionality to see how closely it matches your organization's process and data needs.     

Adapting agile development practices for hyper-agile environments: lessons learned from a COVID-19 emergency response research project

Information Technology and Management - Agile development is known for efficient software development practices that enable teams to quickly develop software to cope with changing requirements....     

Strategic scanning project failure and abandonment factors: lessons learned

This article aims to explore factors contributing to the failure and abandonment of strategic scanning projects and operational systems. Thirty-nine projects that were studied using action research methods are analyzed. The results suggest that failure and abandonment are provoked by combinations of factors relating to stakeholders' qualifications and experience, to the management and organization of the project system, as well as to strategic alignment and changes in the organization's internal structure. These factors include poor project impetus, uninvolved management, unqualified people, inaccurate expectations, project mismanagement, strategy misalignment, poor participation, hostile culture, insufficient budget, conflating technical and managerial problems, previous project trauma and underestimated complexity.     

Restoring a legacy: lessons learned

Rebuilding a legacy system has some parallels to the restoration of a work of art. The authors draw upon this comparison to illustrate the challenges they faced in redesigning a telephony system. The restoration involved far more than updating the code, the development team also had to understand the existing architecture, add new functionality, and develop a long-term hardware migration plan. The technical and managerial lessons learned should prove valuable to those involved in similar projects     

Applying empirical software engineering to software architecture: challenges and lessons learned

In the last 15 years, software architecture has emerged as an important software engineering field for managing the development and maintenance of large, software-intensive systems. Software architecture community has developed numerous methods, techniques, and tools to support the architecture process (analysis, design, and review). Historically, most advances in software architecture have been driven by talented people and industrial experience, but there is now a growing need to systematically gather empirical evidence about the advantages or otherwise of tools and methods rather than just rely on promotional anecdotes or rhetoric. The aim of this paper is to promote and facilitate the application of the empirical paradigm to software architecture. To this end, we describe the challenges and lessons learned when assessing software architecture research that used controlled experiments, replications, expert opinion, systematic literature reviews, observational studies, and surveys. Our research will support the emergence of a body of knowledge consisting of the more widely-accepted and well-formed software architecture theories.     

Groups at work: lessons from research into large-scale coordination

Study of large-scale coordination seeks us to understand work between and across groups beyond earlier conceptions of social human factors. Revealing the complexity of work domains and the subtle yet effective ways that workers negotiate its challenges opens the way to develop support tools that are grounded in a scientific understanding. This first special issue in Large-Scale Coordination shows the breadth of application research from air traffic control to the military and multi-agency emergency response. A second special issue later this year will show depth of research at large scale in healthcare.     

Efficient data management in a large-scale epidemiology research project

This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine". The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.     

Rapid prototyping: lessons learned

Opinions on rapid prototyping as a practical development tool vary widely, with conventional wisdom seeing it more as a research topic than a workable method. The authors counter this notion with results from 39 case studies, most of which have used this approach successfully     

MySQL: lessons learned on a digital library

In recent years, the open source products have achieved enterprise-level quality. In response, enterprises have become more interested in migrating from proprietary, commercial products to open source. Businesses around the world commonly use the two leading open source relational DBMSs, MySQL and PostgreSQL. The Los Alamos National Laboratory's Research Library has used MySQL databases for years. However, a recent project to develop a comprehensive database of scientific journal articles and citation information revealed its unique strengths and features.     

UML formal semantics: lessons learned

The article below presents the insights gained during a number of years of research dedicated to the formalisation of the Unified Modeling Language.     

Usability testing in a competive market: lessons learned

Abstract

Usability testing is a relatively new and rapidly developing field. Newcomers to usability testing typically enter the profession with a knowledge of social science research methods and the belief that usability testing is conducted in a lab using thinking aloud techniques to identify usability defects. Our practice of usability testing at Mead Data Central has shown that these core beliefs represent an approach of limited utility in designing products like the LEX1S®NEXIS® research systems. In this paper, we describe our experiences in conducting traditional usability testing and how we used the results of those efforts to develop more effective methods of testing for Mead Data Central's products and customers.     

Cognitive skills training: lessons learned

This paper describes lessons we have learned about presenting cognitive skills training. We have used ShadowBox as our training approach (Klein and Borders in J Cogn Eng Decis Mak 10:268–280, 2016), but the lessons apply regardless of specific techniques employed. We analyze key takeaways and lessons learned throughout the course of multiple ShadowBox projects. We explain how the original ShadowBox mission statement has evolved based on these lessons learned. Recommendations are offered for others who are engaged in cognitive skills training.     

Interactive remote instruction: lessons learned

This paper describes experiences with the use of the interactive remote instruction (IRI) system which was used to teach a computer science graduate course at Old Dominion University during the Fall 1995 semester. Through the use of high-speed networks and high performance workstations, IRI creates a virtual classroom so that geographically dispersed students can fully participate in a class. Central to this system is the availability of a personal workstation to each student through which the class is taken. Placing a workstation at each student's disposal opens the opportunity for student interaction and group collaboration. We compare this approach with other educational settings and describe lessons learned from instructor, student and technical perspectives. We also describe changes we are putting in place for an expanded usage in the coming fall semester using fiberoptic cable supporting a WAN internet protocol.     

Design measurement: some lessons learned

The author extracts from several measurement projects some of the important lessons he has learned about measurement in general and design measurement in particular. He synthesizes from these lessons a design-measurement framework in an attempt to communicate his personal measurement experience to other software engineers. The lessons he has learned fall into three areas: (1) how measurement must be applied in individual experiments or case studies; (2) how measurement can help continuously improve an organization's state of the practice; and (3) why measurement requires automated support.<>     

Healthcare groups at work: further lessons from research into large-scale coordination

The first special issue in Large-Scale Coordination earlier this year showed the breadth of application research from air traffic control to the military and multi-agency emergency response. This special issue shows depth of research at large scale in healthcare. These studies of large-scale coordination examine work between and across groups, beyond earlier conceptions of social human factors. Revealing the complexity of work domains and the subtle, yet effective, ways that workers negotiate its challenges opens the way to develop information and communications technology support tools that are grounded in a scientific understanding.     

Handhelds go to school: lessons learned

Working in conjunction with teachers, researchers have developed a series of projects exploring the potential for using wireless handheld devices to enhance K-12 classroom instruction.     

Teaching PSP: challenges and lessons learned

Software engineering educators need to provide environments where students learn about the size and complexity of modern software systems and the techniques available for managing these difficulties. Five universities used the personal software process to teach software engineering concepts in a variety of contexts.     

Ada and real-time robotics: lessons learned

The Supervisory Telerobotics Laboratory (Steler) at the Jet Propulsion Laboratory (JPL) has developed a prototype telerobotic system to demonstrate the type of technology that will be used aboard Space Station Freedom Although the concept of telerobotics for space exploration is not new, the Steler system broke new ground in its demonstration of the feasibility of a local-remote architecture, including ground-remote control, for space applications. A remote space environment poses some interesting computational challenges to ground-remote control of space applications. A remote site has limited computation facilities, is burdened by minimal bandwidth and by round-trip communication delays of as much as 8 seconds, and yet is expected to respond quickly, predictably, and with recoverability to any anomalous situation. Additional design constraints are imposed by NASA's limitations on software, including on-board programs for the robot, being uploaded to the remote site without prior flight qualification. The authors discuss the use of Ada to meet the computational requirements of this project