Pseudo dynamic metrics

Gunnalan, R.; Shereshevsky, M.; Ammar, H.H.

doi:10.1109/aiccsa.2005.1387106

Cited by 14 publications

(13 citation statements)

References 14 publications

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“…Current agile productivity measures are not suitable for all roles and they only concentrate on coding (development) [24]. One should better strive for balance on what should be measured and how much value the measure brings up.…”

Section: Discussionmentioning

confidence: 99%

Measuring productivity in agile software development process: a scoping study

Shah¹,

Papatheocharous²,

Nyfjord³

2015

Proceedings of the 2015 International Conference on Software and System Process

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An agile software development process is often claimed to increase productivity. However, productivity measurement in agile software development is little researched. Measures are not explicitly defined nor commonly agreed upon. In this paper, we highlight the agile productivity measures reported in literature by means of a research method called scoping study. We were able to identify 12 papers reporting the productivity measures in agile software development processes. We found that finding, understanding and putting into use agile productivity definitions is not an easy task. From the perspective of common roles in agile software development process and existing knowledge workers' productivity dimensions, we also emphasize that none of the productivity measures satisfy these fully. We recommend that future effort should be focused on defining agile productivity in measurable, practicable and meaningful form.

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Section: Discussionmentioning

confidence: 99%

Measuring productivity in agile software development process: a scoping study

Shah¹,

Papatheocharous²,

Nyfjord³

2015

Proceedings of the 2015 International Conference on Software and System Process

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“…In this paper we build on the view of Basili et al (1996) that traditional static software metrics may be necessary but not sufficient for characterising, assessing and predicting the entire quality profile of OO systems, and so we propose the use of dynamic metrics to represent further characteristics. Dynamic metrics are the sub-class of software measures that capture the dynamic behaviour of a software system and have been shown to be related to software quality attributes (Cai, 2008, Gunnalan et al, 2005, Scotto et al, 2006. Consideration of this group of metrics provides a more complete insight into the multiple dimensions of software quality when compared to static metrics alone (Dufour et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Consideration of this group of metrics provides a more complete insight into the multiple dimensions of software quality when compared to static metrics alone (Dufour et al, 2003). Dynamic metrics are usually computed based on data collected during program execution (i.e., at runtime) and may be obtained from the execution traces of the code (Gunnalan et al, 2005), although in some cases simulation can be used instead of the actual execution. Therefore they can directly reflect the quality attributes of that program, product or system in operation.…”

Section: Introductionmentioning

confidence: 99%

Understanding Class-level Testability Through Dynamic Analysis

Tahir

MacDonell

Buchan

2014

Proceedings of the 9th International Conference on Evaluation of Novel Approaches to Software Engineering

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Abstract:It is generally acknowledged that software testing is both challenging and time-consuming. Understanding the factors that may positively or negatively affect testing effort will point to possibilities for reducing this effort. Consequently there is a significant body of research that has investigated relationships between static code properties and testability. The work reported in this paper complements this body of research by providing an empirical evaluation of the degree of association between runtime properties and class-level testability in object-oriented (OO) systems. The motivation for the use of dynamic code properties comes from the success of such metrics in providing a more complete insight into the multiple dimensions of software quality. In particular, we investigate the potential relationships between the runtime characteristics of production code, represented by Dynamic Coupling and Key Classes, and internal class-level testability. Testability of a class is considered here at the level of unit tests and two different measures are used to characterise those unit tests. The selected measures relate to test scope and structure: one is intended to measure the unit test size, represented by test lines of code, and the other is designed to reflect the intended design, represented by the number of test cases. In this research we found that Dynamic Coupling and Key Classes have significant correlations with class-level testability measures. We therefore suggest that these properties could be used as indicators of class-level testability. These results enhance our current knowledge and should help researchers in the area to build on previous results regarding factors believed to be related to testability and testing. Our results should also benefit practitioners in future class testability planning and maintenance activities.

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“…This gap between static and dynamic metrics can be narrowed down by using the concept of pseudo dynamic metrics. Pseudo dynamic metrics are the static metrics adjusted to reflect the operational profile of the expected usage [59] . Operational profiles can be estimated from the analysis of source code or UML design specifications [49] and do not require execution traces.…”

Section: Pseudo Dynamic Metricsmentioning

confidence: 99%

A Survey of Dynamic Software Metrics

Chhabra

Gupta

2010

J. Comput. Sci. Technol.

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Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions. However, conventional static metrics have been found to be inadequate for modern object-oriented software due to the presence of object-oriented features such as polymorphism, dynamic binding, inheritance and unused code. This fact motivates us to focus on dynamic metrics in place of traditional static metrics. Moreover, dynamic metrics are more precise than static metrics as they are able to capture the dynamic behaviour of the software system during measurement. These dynamic metrics are usually obtained from the execution traces of the code or from the executable models. In this paper, advantages of dynamic metrics over static metrics are discussed and then a survey of the existing dynamic metrics is carried out. These metrics are characterized into different categories such as dynamic coupling metrics, dynamic cohesion metrics. Towards end of the paper, potential research challenges and opportunities in the field of dynamic metrics are identified.

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Pseudo dynamic metrics

Cited by 14 publications

References 14 publications

Measuring productivity in agile software development process: a scoping study

Measuring productivity in agile software development process: a scoping study

Understanding Class-level Testability Through Dynamic Analysis

A Survey of Dynamic Software Metrics

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