Systems Engineering Leading Indicators Guide, Version 2.0

The Systems Engineering Leading Indicators Guide editorial team is pleased to announce the release of Version 2.0. Version 2.0 supersedes Version 1.0, which was released in July 2007 and was the result of a project initiated by the Lean Advancement Initiative (LAI) at MIT in cooperation with: the International Council on Systems Engineering (INCOSE), Practical Software and Systems Measurement (PSM), and the Systems Engineering Advancement Research Initiative (SEAri) at MIT. A leading indicator is a measure for evaluating the effectiveness of how a specific project activity is likely to affect system performance objectives. A leading indicator may be an individual measure or a collection of measures and associated analysis that is predictive of future systems engineering performance. Systems engineering performance itself could be an indicator of future project execution and system performance. Leading indicators aid leadership in delivering value to customers and end users and help identify interventions and actions to avoid rework and wasted effort. Conventional measures provide status and historical information. Leading indicators use an approach that draws on trend information to allow for predictive analysis. By analyzing trends, predictions can be forecast on the outcomes of certain activities. Trends are analyzed for insight into both the entity being measured and potential impacts to other entities. This provides leaders with the data they need to make informed decisions and where necessary, take preventative or corrective action during the program in a proactive manner. Version 2.0 guide adds five new leading indicators to the previous 13 for a new total of 18 indicators. The guide addresses feedback from users of the previous version of the guide, as well as lessons learned from implementation and industry workshops. The document format has been improved for usability, and several new appendices provide application information and techniques for determining correlations of indicators. Tailoring of the guide for effective use is encouraged. Additional collaborating organizations involved in Version 2.0 include the Naval Air Systems Command (NAVAIR), US Department of Defense Systems Engineering Research Center (SERC), and National Defense Industrial Association (NDIA) Systems Engineering Division (SED). Many leading measurement and systems engineering experts from government, industry, and academia volunteered their time to work on this initiative

Systems Engineering Leading Indicators Guide, Version 1.0

The Systems Engineering Leading Indicators guide set reflects the initial subset of possible indicators that were considered to be the highest priority for evaluating effectiveness before the fact. A leading indicator is a measure for evaluating the effectiveness of a how a specific activity is applied on a program in a manner that provides information about impacts that are likely to affect the system performance objectives. A leading indicator may be an individual measure, or collection of measures, that are predictive of future system performance before the performance is realized. Leading indicators aid leadership in delivering value to customers and end users, while assisting in taking interventions and actions to avoid rework and wasted effort. The Systems Engineering Leading Indicators Guide was initiated as a result of the June 2004 Air Force/LAI Workshop on Systems Engineering for Robustness, this guide supports systems engineering revitalization. Over several years, a group of industry, government, and academic stakeholders worked to define and validate a set of thirteen indicators for evaluating the effectiveness of systems engineering on a program. Released as version 1.0 in June 2007 the leading indicators provide predictive information to make informed decisions and where necessary, take preventative or corrective action during the program in a proactive manner. While the leading indicators appear similar to existing measures and often use the same base information, the difference lies in how the information is gathered, evaluated, interpreted and used to provide a forward looking perspective

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Teaching Software Engineering through Robotics

This paper presents a newly-developed robotics programming course and reports the initial results of software engineering education in robotics context. Robotics programming, as a multidisciplinary course, puts equal emphasis on software engineering and robotics. It teaches students proper software engineering -- in particular, modularity and documentation -- by having them implement four core robotics algorithms for an educational robot. To evaluate the effect of software engineering education in robotics context, we analyze pre- and post-class survey data and the four assignments our students completed for the course. The analysis suggests that the students acquired an understanding of software engineering techniques and principles

Ethical Issues in Empirical Studies of Software Engineering

The popularity of empirical methods in software engineering research is on the rise. Surveys, experiments, metrics, case studies, and field studies are examples of empirical methods used to investigate both software engineering processes and products. The increased application of empirical methods has also brought about an increase in discussions about adapting these methods to the peculiarities of software engineering. In contrast, the ethical issues raised by empirical methods have received little, if any, attention in the software engineering literature. This article is intended to introduce the ethical issues raised by empirical research to the software engineering research community, and to stimulate discussion of how best to deal with these ethical issues. Through a review of the ethical codes of several fields that commonly employ humans and artifacts as research subjects, we have identified major ethical issues relevant to empirical studies of software engineering. These issues are illustrated with real empirical studies of software engineering

Standards of Validity and the Validity of Standards in Behavioral Software Engineering Research: The Perspective of Psychological Test Theory

Background. There are some publications in software engineering research that aim at guiding researchers in assessing validity threats to their studies. Still, many researchers fail to address many aspects of validity that are essential to quantitative research on human factors. Goal. This paper has the goal of triggering a change of mindset in what types of studies are the most valuable to the behavioral software engineering field, and also provide more details of what construct validity is. Method. The approach is based on psychological test theory and draws upon methods used in psychology in relation to construct validity. Results. In this paper, I suggest a different approach to validity threats than what is commonplace in behavioral software engineering research. Conclusions. While this paper focuses on behavioral software engineering, I believe other types of software engineering research might also benefit from an increased focus on construct validity.Comment: ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), Oulu, Finland, October 11-12, 2018. 4 page

Integrating Software Engineering and Usability Engineering

The usability of products gains in importance not only for the users of a system but also for manufacturing organizations. According to Jokela, the advantages for users are far-reaching and include increased productivity, improved quality of work, and increased user satisfaction. Manufacturers also profit significantly through a reduction of support an