Technical debt (TD) is receiving more and more attention to software engineering. Although it was initially used as a communication tool for technical and non-technical stakeholders, nowadays this concept supports the improvement of the software’s internal quality. Despite the increasing number of studies regarding TD and its management, only a few are concerned with the industry. Therefore, this primary study aims to characterize TD and its management under the perspective of Brazilian software organizations using their practitioners as proxies. A survey was performed with 62 practitioners, representing around 12 organizations and 30 software projects. The analysis of 40 valid questionnaires indicates that TD is still unknown to a considerable fraction of the participants, and only a small group of organizations adopt TD management activities in their projects. The survey package is available and can be used to support further investigations on TD management in software organizations.
The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.
Predicting and quantifying promptly the Technical Debt has turned into an issue of significant importance over recent years. In the cloud marketplace, where cloud services can be leased, the difficulty to identify the Technical Debt effectively can have a significant impact. In this chapter, the probability of introducing the Technical Debt due to budget and cloud service selection decisions is investigated. A cost estimation approach for implementing Software as a Service (SaaS) in the cloud is examined, indicating three scenarios for predicting the incurrence of Technical Debt in the future. The Constructive Cost Model (COCOMO) is used in order to estimate the cost of the implementation and define a range of secureness by adopting a tolerance value for prediction. Furthermore, a Technical Debt quantification approach is researched for leasing a cloud Software as a Service (SaaS) in order to provide insights about the most appropriate cloud service to be selected.
A Taste of the Software Industry Perception of Technical Debt and its Management in Brazil
