Metrics and Evolution in Open Source Software

Lee, Young; Yang, Jeong; Chang, Kai H.

doi:10.1109/qsic.2007.4385495

Cited by 28 publications

(28 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study carried out by Lee et al [9], open-source "JFreeChart" software was analyzed to understand the "fanin/out" dependency and compliance metric and software development behavior. They have developed a software called "JamTool" to extract software metric and quality features.…”

Section: Related Workmentioning

confidence: 99%

Determining the Tested Classes with Software Metrics

Yücalar¹,

Borandağ²

2017

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Early detection and correction of errors appearing in software projects reduces the risk of exceeding the estimated time and cost. An efficient and effective test plan should be implemented to detect potential errors as early as possible. In the earlier phases, codes can be analyzed by efficiently employing software metric and insight can be gained about error susceptibility and measures can be taken if necessary. It is possible to classify software metric according to the time of collecting data, information used in the measurement, type and interval of the data generated. Considering software metric depending on the type and interval of the data generated, object-oriented software metric is widely used in the literature. There are three main metric sets used for software projects that are developed as object-oriented. These are Chidamber & Kemerer, MOOD and QMOOD metric sets. In this study, an approach for identifying the classes that should primarily be tested has been developed by using the objectoriented software metric. Then, this approach is applied for selected versions of the project developed. According to the results obtained, the correct determination rate of sum of the metrics method, which was developed to identify the classes that should primarily be tested, is ranged between 55% and 68%. In the random selection method, which was used to make comparisons, the correct determination rate for identifying the classes that should primarily be tested is ranged between 9.23% and 11.05%. In the results obtained using sum of the metrics method, a significant rate of improvement is observed compared to the random selection method.

show abstract

Section: Related Workmentioning

confidence: 99%

Determining the Tested Classes with Software Metrics

Yücalar¹,

Borandağ²

2017

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…Ambu et al [26] address the evolution of quality metrics in an agile/distributed project and investigate how the distribution of the development team has impacted the code quality. Lee et al [9] provide an overview of open source software evolution with software metrics. The authors explored the evolution of an open source software system in terms of size, coupling and cohesion, and discuss its quality change based on the Lehman's laws of evolution [4], [5], [27].…”

Section: Related Workmentioning

confidence: 99%

“…We applied the typical significance threshold (α = 0.05) to decide whether the correlations where significant. A significant correlation between 0.7 and 1.0 (or -0.7 and -1.0) is considered as a strong correlation [9]. class) and the values of OO metrics.…”

Section: A Software Quality Evolutionmentioning

confidence: 99%

Metrics and Software Quality Evolution: A Case Study on Open Source Software

Drouin¹,

Badri²,

Touré³

2013

IJCTE

View full text Add to dashboard Cite

Abstract-This paper aims at analyzing empirically the quality evolution of an open source software using metrics. We used a control flow based metric (Quality Assurance IndicatorQi) which we proposed in a previous work. We wanted to investigate if the Qi metric can be used to observe how quality evolves along the evolution of the successive released versions of the subject software system. We addressed software quality from an internal perspective. We performed an empirical analysis using historical data on the subject system (Apache Tomcat). The collected data cover, in fact, a period of more than seven years (thirty-one versions in total). Empirical results provide evidence that the Qi metric reflects properly the quality evolution of the subject system.

show abstract

“…Inputs can be parameters and global variables that are used. There are two approaches to calculate fan-in coupling: the structural and the informational [15,16]. Informational approach considers data communication (e.g., through parameter passing), whereas structural approach considers exchange of program control (e.g., via function calls or method invocations) [17].…”

Section: Employed Metricsmentioning

confidence: 99%

“…The outputs can be parameters or global variables (modified). There are also two approaches to calculate fan-out coupling: the structural and the informational [15,16].…”

Section: Employed Metricsmentioning

confidence: 99%

An application of data envelopment analysis to software quality assessment

Paschalidou

Stiakakis

Chatzigeorgiou

2013

Proceedings of the 6th Balkan Conference in Informatics

View full text Add to dashboard Cite

Data Envelopment Analysis (DEA) is a non-parametric technique which involves the use of linear programming methods to measure the efficiency of a homogenous set of units. These units are known as Decision Making Units (DMUs) and defined by multiple input and output data. Efficiencies are measured relative to a piece-wise surface (efficient frontier) which envelops the data, thus justifying the name of the technique. Although DEA has been mostly used in production economics, its application in the context of software quality evaluation seems to be a promising approach. This study provides an application of DEA to assess the evolution of two open-source software projects in terms of selected metric values for successive versions of each project. What is really interesting in DEA is that a single efficiency score is calculated for each version despite the often convoluted overall picture of the metric values. According to a simplified view of DEA, there are two categories of units, the efficient (onto the efficient frontier) and the inefficient ones. Each inefficient unit is characterized by a reference set of peers which involves all the efficient units "operating" closer to that unit. Through the consideration of the reference set of the inefficient versions of each project, the metrics that require improvement, as well as the extent of improvement, could be estimated. These results could assist software developers in identifying design issues that require further improvement. Notwithstanding the fact that there are a number of issues to be further investigated, the applicability of DEA and other operations research tools in the context of software quality might yield interesting results.

show abstract

Metrics and Evolution in Open Source Software

Cited by 28 publications

References 9 publications

Determining the Tested Classes with Software Metrics

Determining the Tested Classes with Software Metrics

Metrics and Software Quality Evolution: A Case Study on Open Source Software

An application of data envelopment analysis to software quality assessment

Contact Info

Product

Resources

About