Towards an evidence-based theoretical framework on factors influencing the software development productivity

摘要

Context: Productivity refers to the rate at which a company produces goods, and its observation takes into account the number of people and the amount of other necessary resources to deliver such goods. However, it is not clear how to observe productivity and what influences it when the product is software since most effort spent in software development is creative and human-dependent. Besides, the outputs vary from each instance of software solutions throughout the software development process.Objective: To characterize software development productivity and investigate evidence-based factors aiming at understanding their influence on software development productivity. Method: To evolve and replicate a systematic literature review (SLR) on software development productivity measurement and prediction methods. Next, to use the Structured Synthesis Method to aggregate and describe the relationships among software productivity and correspondingly influence factors according to the results of primary studies selected by SLR protocol. Results: The study allowed organizing a body of knowledge through a model obtained from empirical evidence comprising 25 factors and 33 relationships regarding software development productivity based on the technical literature over the last 30 years. It uses a taxonomy for describing observations and for supporting the reasoning of uncertainty on the evidence regarding software development productivity in Software Engineering. Conclusions: The acquired knowledge may represent a first try towards a well-grounded theoretical framework regarding software development productivity. Based on a methodically selected set of evidence, the proposed framework intends to support practitioners and researchers on observing, deciding, and controlling software development productivity in software projects. Additionally, it can encourage researchers to identify which phenomena deserve better understanding and explanation through further empirical studies.