Blinded by Simplicity: Locating the Social Dimension in Software Development Process Literature

The software development process is a complex human, intellectual and labor-intensive activity and human related factors have shown to be the most significant contributors to software system failures. Lacking the ability to identify or quantify these factors, software practitioners will not learn from the failures caused by them. Although, social factors give rise to high failure rates in software development projects they tend to be ignored. Business continues as usual. The inability for software engineers to attain a holistic and inclusive approach will leave the social dimension out and undermine the realization of a fully sustainable software development process.This paper builds on the master’s thesis with the same title completed in December 2019 at Stockholm University. The thesis demonstrates how research literature on software development processes addresses (or not) the social dimension of sustainability from a holistic point of view. The results indicate that the practice of dealing holistically with complexity including the social dimension is still underdeveloped. Further research is suggested regarding the development of adequate supporting tools, social skills, and managerial attitudes and behaviors

The FIELDS Instrument Suite for Solar Probe Plus: Measuring the Coronal Plasma and Magnetic Field, Plasma Waves and Turbulence, and Radio Signatures of Solar Transients.

NASA's Solar Probe Plus (SPP) mission will make the first in situ measurements of the solar corona and the birthplace of the solar wind. The FIELDS instrument suite on SPP will make direct measurements of electric and magnetic fields, the properties of in situ plasma waves, electron density and temperature profiles, and interplanetary radio emissions, amongst other things. Here, we describe the scientific objectives targeted by the SPP/FIELDS instrument, the instrument design itself, and the instrument concept of operations and planned data products

Challenges in Evolving a Large Scale Software Product

Evolving a large system presents a number of signi cant challenges. Not only is the developer concerned about how to t in a new feature to a maze of existing features, he has to make surehischanges do not con ict with those being made in parallel by his colleagues. This is a minor problem in small projects with small organizations. However, as the project size scales up, so does the organization, and management of parallel tracks of development becomes a major concern. Moreover, increasing usage by customers with diverse needs pulls the evolving software into di erent directions, necessitating the evolution of multiple customized versions and compounding the already complex problem of evolving legacy systems. We will examine one such legacy system, the Lucent Technologies 5ESS R switching system. First introduced in 1982, 5ESS was envisioned to support telecommunication needs well into the next century. Already one of the largest and most complex pieces of real time code in the world, the software to run the switch still continues to evolve with new features and in an increasing number of customized versions. In order to keep up with future evolution and maintain the growing base of customers, a combined procedural and technological solution was put in place. We will discuss this particular solution and its limitations.

Understanding the Effects of Developer Activities on Inspection Interval

We have conducted an industrial experiment to assess the cost-benefit tradeoffs of several software inspection processes. Our results to date explain the variation in observed effectiveness very well, but are unable to satisfactorily explain variation in inspection interval. In this article we examine the effect of a new factor -- process environment -- on inspection interval (calendar time needed to complete the inspection). Our analysis suggests that process environment does indeed influence inspection interval. In particular, we found that nonuniform work priorities, time-varying workloads, and deadlines have significant effects. Moreover, these experiences suggest that regression models are inherently inadequate for interval modeling, and that queueing models may be more effective