A Statistical Model to Assess the Team's Productivity in Agile Software Teams

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. Software team productivity measurement is essential in agile teams to increase the performance of software development. Due to the prevalence of agile methodologies and increasing competition of software development companies, software team productivity has become one of the crucial challenges for agile software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team’s productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on the team’s productivity. Moreover, the results also state that inter-team relations affect the most on software teams’ productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors.

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC.

A Statistical Model to Assess the Team's Productivity in Agile Software Teams

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. The software team’s productivity measurement is essential in agile teams to increase the performance of Software development. Due to the increasing competition of software development companies, software team productivity has become one of the crucial challenges for software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team’s productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on team productivity. Moreover, the results also state that inter-team relations affect the most on software teams’ productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors.

A Statistical Model to Compute the Effect of Agile Teams’ Productivity

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important model that has spread through the world of software development over the past decade. Software productivity measurement is essential in agile teams to increase the performance of Software development. Due to the increasing competition of software development companies, software team productivity has become one of the crucial challenges for software companies and teams. Awareness of the level of team productivity can help them to achieve more accurate estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to evaluate the productivity of the software in an up-to-date view of software development and to present a model for computing software team productivity. A survey was conducted with forty software development organizations located in Iran and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had an impact on productivity. Moreover, the results also state that inter-team relations affect the most on software teams’ productivity. Finally, using the model fit test, it found that 80% of productivity changes based on team effectiveness factors.

Software Productivity Trends and Issues

The Department of Defense is experiencing an explosive increase in its demand for software implemented features in weapon systems. The combination of exponential increases in computing power and similar advances in memory density and speed has made software mediated implementation of system features increasingly attractive. In the meantime, defense software productivity and industrial base capacity have not been growing as quickly as demand. This article uses the limited data that exist regarding defense software supply, demand, and productivity trends to estimate the severity of the capacity bottleneck, then briefly discusses the potential actions available to the Department to mitigate that bottleneck in the long run.

Understanding the landscape of scientific software used on high-performance computing platforms

Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific computing. In this survey paper, we examine a modest set of HPC scientific simulation applications that are already using cutting-edge HPC platforms. Several have been in existence for a decade or more. Our objective in this survey is twofold: first, to understand the landscape of scientific computing on HPC platforms in order to distill the currently scattered knowledge about software practices that have helped both developer and software productivity, and second, to understand the kind of tools and methodologies that need attention for continued productivity.