Developing an h-index for OSS developers

The public data available in Open Source Software (OSS) repositories has been used for many practical reasons: detecting community structures; identifying key roles among developers; understanding software quality; predicting the arousal of bugs in large OSS systems, and so on; but also to formulate and validate new metrics and proof-of-concepts on general, non-OSS specific, software engineering aspects. One of the results that has not emerged yet from the analysis of OSS repositories is how to help the “career advancement” of developers: given the available data on products and processes used in OSS development, it should be possible to produce measurements to identify and describe a developer, that could be used externally as a measure of recognition and experience. This paper builds on top of the h-index, used in academic contexts, and which is used to determine the recognition of a researcher among her peers. By creating similar indices for OSS (or any) developers, this work could help defining a baseline for measuring and comparing the contributions of OSS developers in an objective, open and reproducible way

Comparative study of software metrics' aggregation techniques

While software metrics are commonly used to assess software maintainability and study software evolution, they are usually defined on a micro-level (method, class, package). Metrics should therefore be aggregated in order to provide insights in the evolution at the macro-level (system). In addition to traditional aggregation techniques such as the mean, recently econometric aggregation techniques such as the Gini index and the Theil index have been proposed. Advantages and disadvantages of different aggregation techniques have not been evaluated empirically so far. In this paper we present the preliminary results of the comparative study of different aggregation techniques

Web service QoS prediction using improved software source code metrics

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services' QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics

A Pragmatic Means for Measuring the Complexity of Source Code Ensembles

Abstract-Most of the software metrics known and applied today are measured on a per file or even per function basis so that it is difficult to interpret their results for higher-order code ensembles such as components or whole systems. In order to overcome this weakness, we propose the hm-Index as a simple metric to condense the dependencies, i.e. the Fan-out, between source units in such code ensembles into a single number. As it is inspired by the h-Index in bibliometrics, it is based on a wellknown procedure that already had significant impact in a different field. We expect the hm-Index to become a simple metric for comparing the code complexity of different components or systems in software engineering and present promising preliminary results from real-world systems confirming our assumption in this paper

An empirical model for continuous and weighted metric aggregation

International audienceIt is now understood that software metrics alone are not enough to characterize software quality. To cope with this problem, most of advanced and/or industrially validated quality models aggregate software metrics: for example, cy- clomatic complexity is combined with test coverage to stress the fact that it is more important to cover complex methods than accessors. Yet, aggregating and weighting metrics to produce quality indexes is a difficult task. Indeed certain weighting approaches may lead to abnormal situations where a developer increasing the quality of a software component sees the overall quality degrade. Finally, mapping combinations of metric values to quality indexes may be a problem when using thresholds. In this paper, we present the problems we faced when designing the Squale quality model, then we present an empirical solution based on weighted aggregations and on continuous functions. The solution has been termed the Squale quality model and validated over 4 years with two large multinational companies: Air France-KLM and PSA Peugeot- Citroen

Theil index for aggregation of software metrics values

We propose a new approach to aggregating software metrics from the micro-level of individual artifacts (e.g., methods, classes and packages) to the macro-level of the entire software system. The approach, Theil index, is a well-known econometric measure of inequality. The Theil index allows to study the impact of different categorizations of the artifacts, e.g., based on the development technology or developers' teams, on the inequality of the metrics values measured. We apply the Theil index in a series of experiments. We have observed that the Theil index and the related notions provide valuable insights in organization and evolution of software systems, as well as in sources of inequality

Theil index for aggregation of software metrics values

We propose a new approach to aggregating software metrics from the micro-level of individual artifacts (e.g., methods, classes and packages) to the macro-level of the entire software system. The approach, Theil index, was developed for measuring economic inequality. In addition to measuring inequality for all system artifacts Theil index allows reasoning about inequality between groups of artifacts, e.g., implemented in different programming languages or by different teams