Ioannis Stamelos

Code quality analysis in open source software development

Abstract Proponents of open source style software development claim that better software is produced using this model compared with the traditional closed model. However, there is little empirical evidence in support of these claims. In this paper, we present the results of a pilot case study aiming: (a) to understand the implications of structural quality; and (b) to figure out the benefits of structural quality analysis of the code delivered by open source style development. To this end, we have measured quality characteristics of 100 applications written for Linux, using a software measurement tool, and compared the results with the industrial standard that is proposed by the tool. Another target of this case study was to investigate the issue of modularity in open source as this characteristic is being considered crucial by the proponents of open source for this type of software development. We have empirically assessed the relationship between the size of the application components and the delivered quality measured through user satisfaction. We have determined that, up to a certain extent, the average component size of an application is negatively related to the user satisfaction for this application.

A Simulation Tool for Efficient Analogy Based Cost Estimation

Estimation of a software project effort, based on project analogies, is a promising method in the area of software cost estimation. Projects in a historical database, that are analogous (similar) to the project under examination, are detected, and their effort data are used to produce estimates. As in all software cost estimation approaches, important decisions must be made regarding certain parameters, in order to calibrate with local data and obtain reliable estimates. In this paper, we present a statistical simulation tool, namely the bootstrap method, which helps the user in tuning the analogy approach before application to real projects. This is an essential step of the method, because if inappropriate values for the parameters are selected in the first place, the estimate will be inevitably wrong. Additionally, we show how measures of accuracy and in particular, confidence intervals, may be computed for the analogy-based estimates, using the bootstrap method with different assumptions about the population distribution of the data set. Estimate confidence intervals are necessary in order to assess point estimate accuracy and assist risk analysis and project planning. Examples of bootstrap confidence intervals and a comparison with regression models are presented on well-known cost data sets.

On the use of Bayesian belief networks for the prediction of software productivity

Abstract In spite of numerous methods proposed, software cost estimation remains an open issue and in most situations expert judgment is still being used. In this paper, we propose the use of Bayesian belief networks (BBNs), already applied in other software engineering areas, to support expert judgment in software cost estimation. We briefly present BBNs and their advantages for expert opinion support and we propose their use for productivity estimation. We illustrate our approach by giving two examples, one based on the COCOMO81 cost factors and a second one, dealing with productivity in ERP system localization.

The SQO-OSS Quality Model: Measurement Based Open Source Software Evaluation

Software quality evaluation has always been an important part of software business. The quality evaluation process is usually based on hierarchical quality models that measure various aspects of software quality and deduce a characterization of the product quality being evaluated. The particular nature of open source software has rendered existing models inappropriate for detailed quality evaluations. In this paper, we present a hierarchical quality model that evaluates source code and community processes, based on automatic calculation of metric values and their correlation to a set of predefined quality profiles.1

Software productivity and effort prediction with ordinal regression

Abstract In the area of software cost estimation, various methods have been proposed to predict the effort or the productivity of a software project. Although most of the proposed methods produce point estimates, in practice it is more realistic and useful for a method to provide interval predictions. In this paper, we explore the possibility of using such a method, known as ordinal regression to model the probability of correctly classifying a new project to a cost category. The proposed method is applied to three data sets and is validated with respect to its fitting and predictive accuracy.

Building a software cost estimation model based on categorical data

The paper explores the possibility of generating a multi-organisational software cost estimation model by analysing the software cost data collected by the International Software Benchmarking Standards Group. This database contains data about recently developed projects characterised mostly by attributes of categorical nature such as the project business area, organisation type, application domain and usage of certain tools or methods. The generation of the model is based on a statistical technique which has been proposed as alternative to the standard regression approach, namely the categorical regression or regression with optimal scaling. This technique starts with the quantification of the qualitative attributes (expressed either on nominal or ordinal scale), that appear frequently within such data, and proceeds by using the obtained scores as independent variables of a regression model. The generated model is validated by measuring certain indicators of accuracy.

Open source software development should strive for even greater code maintainability

A study of almost six million lines of code tracks how freely accessible source code holds up against time and multiple iterations.

Identifying knowledge brokers that yield software engineering knowledge in OSS projects

Much research on open source software development concentrates on developer lists and other software repositories to investigate what motivates professional software developers to participate in open source software projects. Little attention has been paid to individuals who spend valuable time in lists helping participants on some mundane yet vital project activities. Using three Debian lists as a case study we investigate the impact of knowledge brokers and their associated activities in open source projects. Social network analysis was used to visualize how participants are affiliated with the lists. The network topology reveals substantial community participation. The consequence of collaborating in mundane activities for the success of open source software projects is discussed. The direct beneficiaries of this research are in the identification of knowledge experts in open source software projects. � 2006 Elsevier B.V. All rights reserved.

Evaluating the Quality of Open Source Software

Traditionally, research on quality attributes was either kept under wraps within the organization that performed it, or carried out by outsiders using narrow, black-box techniques. The emergence of open source software has changed this picture allowing us to evaluate both software products and the processes that yield them. Thus, the software source code and the associated data stored in the version control system, the bug tracking databases, the mailing lists, and the wikis allow us to evaluate quality in a transparent way. Even better, the large number of (often competing) open source projects makes it possible to contrast the quality of comparable systems serving the same domain. Furthermore, by combining historical source code snapshots with significant events, such as bug discoveries and fixes, we can further dig into the causes and effects of problems. Here we present motivating examples, tools, and techniques that can be used to evaluate the quality of open source (and by extension also proprietary) software.

An experimental investigation of personality types impact on pair effectiveness in pair programming

In this paper, pair programming is empirically investigated from the perspective of developer personalities and temperaments and how they affect pair effectiveness. A controlled experiment was conducted to investigate the impact of developer personalities and temperaments on communication, pair performance and pair viability-collaboration. The experiment involved 70 undergraduate students and the objective was to compare pairs of heterogeneous developer personalities and temperaments with pairs of homogeneous personalities and temperaments, in terms of pair effectiveness. Pair effectiveness is expressed in terms of pair performance, measured by communication, velocity, design correctness and passed acceptance tests, and pair collaboration-viability measured by developers’ satisfaction, knowledge acquisition and participation. The results have shown that there is important difference between the two groups, indicating better communication, pair performance and pair collaboration-viability for the pairs with heterogeneous personalities and temperaments. In order to provide an objective assessment of the differences between the two groups of pairs, a number of statistical tests and stepwise Discriminant Analysis were used.