In this article, author talks about how to improve the accuracy of software development effort estimations. He suggests to use relevant historical data improve estimation accuracy and to avoid early estimates based on incomplete information. He also discusses how to measure and predict productivity in software projects.
In this IEEE article, authors provide an overview of current technologies for crowdsourcing in software development. They talk about the requirements, current practice and trends in collaborative platforms.
Search engines are developed using standard sets of test cases to measure the effectiveness of alternative approaches. This article talks about TREC project used to measure quality of search results.
In this article, authors talk about the impact of pattern languages on software design community over past 20 years. 1
In this article, authors discuss Morphosis, a multi-perspective measuring approach for architecture sustainability that includes compliance checking and tracking of architecture-level code metrics.
In this article, based on a research study the authors discuss the criteria that can help architects assess architectural design decisions’ sustainability.
In this article, authors present an empirical study about the software architecture practices for managing non-functional requirements and decision making in software development processes. 2
In this article, author discusses the effectiveness of using design patterns, based on surveys conducted to indicate which patterns were considered useful under what circumstances. 12
This article describes the relationship between architecture and process of software development and how architecture can respond to functional requirements and developer habitability. 1
This article shares a simple technique used to address problems such as a system’s intended functionality but also qualities such as performance, reliability, portability, and availability.
In this article, author talks about the need for change in predictive modeling focus and compares four types of data mining:algorithm mining, landscape mining, decision mining and discussion mining.