Object-oriented dynamic complexity measures for software understandability

2017 ◽  
Vol 13 (2-3) ◽  
pp. 177-190 ◽  
Author(s):  
Anjana Gosain ◽  
Ganga Sharma
Keyword(s):  
Object Oriented ◽  
Dynamic Complexity ◽  
Complexity Measures

Dynamic Complexity Measures for Use in Complexity-Based System Design

2017 ◽  
Vol 11 (4) ◽  
pp. 2018-2027 ◽  
Author(s):  
Jonathan Fischi ◽  
Roshanak Nilchiani ◽  
Jon Wade
Keyword(s):  
System Design ◽  
Dynamic Complexity ◽  
Complexity Measures

Object-Oriented Cognitive Complexity Measures

2017 ◽  
pp. 917-940
Author(s):  
Sanjay Misra ◽  
Adewole Adewumi
Keyword(s):  
Software Development ◽  
Comparative Study ◽  
Critical Review ◽  
Cognitive Complexity ◽  
Object Oriented ◽  
Point Of View ◽  
Complexity Measures ◽  
Cognitive Informatics ◽  
Validation Criteria ◽  
Object Oriented Metrics

This chapter presents the analysis of ten recently proposed object-oriented metrics based on cognitive informatics. The metrics based on cognitive informatics use cognitive weight. Cognitive weight is the representation of the understandability of the piece of software that evaluates the difficulty experienced in comprehending and/or performing the piece of software. Development of metrics based on Cognitive Informatics (CI) is a new area of research, and from this point of view, for the analysis of these metrics, it is important to know their acceptability from other existing evaluation and validation criteria. This chapter presents a critical review on existing object-oriented cognitive complexity measures. In addition, a comparative study based on some selected attributes is presented.

Evaluation of object-oriented spatial complexity measures

2009 ◽  
Vol 34 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Jitender Kumar Chhabra ◽  
Varun Gupta
Keyword(s):  
Object Oriented ◽  
Complexity Measures ◽  
Spatial Complexity

SECTOR COMPLEXITY MEASURES: A COMPARISON

2015 ◽  
Vol 76 (11) ◽  
Author(s):  
Siti Mariam Abdul Rahman ◽  
Clark Borst ◽  
Max Mulder ◽  
Rene van Paassen
Keyword(s):  
Traffic Management ◽  
Linear Regression Analysis ◽  
Solution Space ◽  
Deep Understanding ◽  
Dynamic Complexity ◽  
Complexity Measures ◽  
Human In The Loop ◽  
Traffic Conflicts ◽  
Machine Systems ◽  
Operator Workload

In developing a more advanced human-machine systems for future Air Traffic Management (ATM) concepts requires a deep understanding of what constitutes operator workload and how taskload and sector complexity can affect it. Many efforts have been done in the past to measure and/or predict operator workload using sector complexity. However, most sector complexity metrics that include sector design are calculated according to a set of rules and subjective weightings, rendering them to be dependent of sector. This research focuses on comparing the Solution Space Diagram (SSD) method with a widely accepted complexity metric: Dynamic Density (DD). In essence, the SSD method used in this research, observed aircraft restrictions and opportunities to resolve traffic conflicts in both the speed and heading dimensions. It is hypothesized that the more area covered on the solution space, that is, the fewer options the controller has to resolve conflicts, the more difficult the task and the higher the workload experienced by the controller. To compare sector complexity measures in terms of their transferability in capturing dynamic complexity across different sectors, a human-in-the-loop experiment using two distinct sectors has been designed and conducted. Based on the experiments, it is revealed that the SSD metric has a higher correlation with the controllers' workload ratings than the number of aircraft and the un-weighted NASA DD metric. Although linear regression analysis improved the correlation between the workload ratings and the weighted DD metric as compared to the SSD metric, the DD metric proved to be more sensitive to changes in sector layout than the SSD metric. This result would indicate that the SSD metric is better able to capture controller workload than the DD metric, when tuning for a specific sector layout is not feasible. 

scholarly journals Dynamic Complexity Measures: Definition and Calculation

2018 ◽  
Author(s):  
José Roberto C. Piqueira
Keyword(s):  
Information Source ◽  
Dynamical Variable ◽  
Measured Parameter ◽  
Time Interval ◽  
Informational Entropy ◽  
Economic Data ◽  
Dynamic Complexity ◽  
Complexity Measures ◽  
The Individual ◽  
Individual Information

This work is a generalization of the Lopez-Ruiz, Mancini and Calbet (LMC); and Shiner, Davison and Landsberg (SDL) complexity measures, considering that the state of a system or process is represented by a dynamical variable during a certain time interval. As the two complexity measures are based on the calculation of informational entropy, an equivalent information source is defined and, as time passes, the individual information associated to the measured parameter is the seed to calculate instantaneous LMC and SDL measures. To show how the methodology works, an example with economic data is presented.

Object-Oriented Cognitive Complexity Measures

2015 ◽  
pp. 150-170 ◽  
Author(s):  
Sanjay Misra ◽  
Adewole Adewumi
Keyword(s):  
Software Development ◽  
Comparative Study ◽  
Critical Review ◽  
Cognitive Complexity ◽  
Object Oriented ◽  
Point Of View ◽  
Complexity Measures ◽  
Cognitive Informatics ◽  
Validation Criteria ◽  
Object Oriented Metrics

This chapter presents the analysis of ten recently proposed object-oriented metrics based on cognitive informatics. The metrics based on cognitive informatics use cognitive weight. Cognitive weight is the representation of the understandability of the piece of software that evaluates the difficulty experienced in comprehending and/or performing the piece of software. Development of metrics based on Cognitive Informatics (CI) is a new area of research, and from this point of view, for the analysis of these metrics, it is important to know their acceptability from other existing evaluation and validation criteria. This chapter presents a critical review on existing object-oriented cognitive complexity measures. In addition, a comparative study based on some selected attributes is presented.

Using Z to specify object-oriented software complexity measures

1997 ◽  
Vol 39 (8) ◽  
pp. 515-529 ◽  
Author(s):  
Timothy K. Shih ◽  
Chun-Chia Wang ◽  
Chi-Ming Chung
Keyword(s):  
Object Oriented ◽  
Complexity Measures ◽  
Software Complexity
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