Lefteris Angelis

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.

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.

Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm

Software Cost Estimation can be described as the process of predicting the most realistic effort required to complete a software project. Due to the strong relationship of accurate effort estimations with many crucial project management activities, the research community has been focused on the development and application of a vast variety of methods and models trying to improve the estimation procedure. From the diversity of methods emerged the need for comparisons to determine the best model. However, the inconsistent results brought to light significant doubts and uncertainty about the appropriateness of the comparison process in experimental studies. Overall, there exist several potential sources of bias that have to be considered in order to reinforce the confidence of experiments. In this paper, we propose a statistical framework based on a multiple comparisons algorithm in order to rank several cost estimation models, identifying those which have significant differences in accuracy, and clustering them in nonoverlapping groups. The proposed framework is applied in a large-scale setup of comparing 11 prediction models over six datasets. The results illustrate the benefits and the significant information obtained through the systematic comparison of alternative methods.

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.

Links between the personalities, views and attitudes of software engineers

Context:: Successful software development and management depends not only on the technologies, methods and processes employed but also on the judgments and decisions of the humans involved. These, in turn, are affected by the basic views and attitudes of the individual engineers. Objective:: The objective of this paper is to establish if these views and attitudes can be linked to the personalities of software engineers. Methods:: We summarize the literature on personality and software engineering and then describe an empirical study on 47 professional engineers in ten different Swedish software development companies. The study evaluated the personalities of these engineers via the IPIP 50-item five-factor personality test and prompted them on their attitudes towards and basic views on their professional activities. Results:: We present extensive statistical analyses of their responses to show that there are multiple, significant associations between personality factors and software engineering attitudes. The tested individuals are more homogeneous in personality than a larger sample of individuals from the general population. Conclusion:: Taken together, the methodology and personality test we propose and the associated statistical analyses can help find and quantify relations between complex factors in software engineering projects in both research and practice.

The Success Factors Powering Industry-Academia Collaboration

Collaboration between industry and academia supports improvement and innovation in industry and helps to ensure industrial relevance in academic research. This article presents an exploratory study of the factors for successful collaboration between industry and academia in software research.