What Does It Take to Develop a Million Lines of Open Source Code?

This article presents a preliminary and exploratory study of the relationship between size, on the one hand, and effort, duration and team size, on the other, for 11 Free/Libre/Open Source Software (FLOSS) projects with current size ranging between between 0.6 and 5.3 million lines of code (MLOC). Effort was operationalised based on the number of active committers per month. The extracted data did not fit well an early version of the closed-source cost estimation model COCOMO for proprietary software, overall suggesting that, at least to some extent, FLOSS communities are more productive than closedsource teams. This also motivated the need for FLOSS-specific effort models. As a first approximation, we evaluated 16 linear regression models involving different pairs of attributes. One of our experiments was to calculate the net size, that is, to remove any suspiciously large outliers or jumps in the growth trends. The best model we found involved effort against net size, accounting for 79 percent of the variance. This model was based on data excluding a possible outlier (Eclipse), the largest project in our sample. This suggests that different effort models may be needed for certain categories of FLOSS projects. Incidentally, for each of the 11 individual FLOSS projects we were able to model the net size trends with very high accuracy (R 2 ≥ 0.98). Of the 11 projects, 3 have grown superlinearly, 5 linearly and 3 sublinearly, suggesting that in the majority of the cases accumulated complexity is either well controlled or don't constitute a growth constraining factor

Refactoring software development process terminology through the use of ontology

In work that is ongoing, the authors are examining the extent of software development process terminology drift. Initial findings suggest there is a degree of term confusion, with the mapping of concepts to terms lacking precision in some instances. Ontologies are concerned with identifying the concepts of relevance to a field of endeavour and mapping those concepts to terms such that term confusion is reduced. In this paper, we discuss how ontologies are developed. We also identify various sources of software process terminology. Our work to date indicates that the systematic development of a software development process ontology would be of benefit to the entire software development community. The development of such an ontology would in effect represent a systematic refactoring of the terminology and concepts produced over four decades of software process innovation

A PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation dif?cult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is dif?cult to determine. In such situations, ef?cient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identi?cation of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is ?exible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could signi?cantly improve the performance of existing estimation models