The Correlation among Cognitive Complexity Metrics in Algorithm Analysis

The purpose of this investigation was to measure whether, after group treatment, open ward hospitalized psychiatric patients would demonstrate an increase in ability to describe other people by means of interpersonal constructs. Measuring the ability to describe other people, or more precisely cognitive complexity, has never been undertaken with a hospitalized psychiatric patient population nor has the effect of group treatment on cognitive complexity been examined. Responses investigated included cognitive complexity before and after four weeks of treatment, variability in description of others depending on sex of the other person and whether he or she was liked or disliked, and improvement after four weeks of treatment and on a two-month follow-up. Cognitive complexity was considered in its possible relationship to symptomatology, intelligence, diagnosis, patients' conceptualization of control of their destiny, improvement after treatment, and certain factors involving group process. Two groups of psychiatric patients on open wards at a VA hospital were compared. One group received Human Relations Training Laboratory exercises and had autonomous group sessions for four weeks. The other group received more traditional Group Psychotherapy and had a therapist present for alternate sessions for four weeks. Subjects were comparable in age, education and intelligence. The cognitive complexity measure was a free response paper and pencil instrument on which subjects were asked to identify and describe two liked males, two liked females, two disliked males and two disliked females. Experimental findings may be summarized as follows: 1. Neither group increased in cognitive complexity after treatment. Human Relations Training Laboratory patients decreased significantly more than Group Psychotherapy patients. 2. Psychiatric patients described liked females with greatest facility, implying that they are most adept in. such interactions. They described liked and disliked males with equal facility and had greatest difficulty describing disliked females. 3. When sex is or is not considered, patients produced a greater variety of constructs to describe people who are liked as opposed to people who are disliked. 4. No significant relationship was found between degree of cognitive complexity and degree of symptomatology. 5. Patients manifested equal degree of symptomatic improvement after both types of treatment as rated by the staff and on self-ratings. 6. A significant correlation between intelligence and cognitive complexity was obtained within the intelligence range represented in a patient population. 7. The Rotter I-E score, which measures a person's sense of control of his overall environment, was not correlated with cognitive complexity, which hypothetically

Research indicates that ratee job experience and performance variability can affect performance judgments and causal attributions (Leventhal et al., 1977; Fusilier & Hitt, 1983; Scott & Hammer, 1975). When prototype expectations concerning ratee characteristics are not met, raters reacting to the cognitive inconsistency may distort ratee performance information (DeNisi, Cafferty, & Meglino, 1984). Because cognitively complex raters view behavior in a multidimensional manner (Schneier, 1977), not through a simplified schemata, cognitive complexity's possible effect on rating accuracy under cognitively inconsistent conditions was investigated. In an initial pilot study, 147 undergraduate volunteers rated the expected performance variability of a professor with either zero, five, or ten years of experience. Experiment 1 utilized a Ratee Experience $\times$ Ratee Performance Variability $\times$ Rater Cognitive Complexity regression design. One-hundred-thirty-five undergraduate volunteers completed three cognitive complexity measures, six vignettes, six BARS, an attribution questionnaire, and a manipulation check questionnaire. Experiment 2 used a similar design with videotaped rating stimuli (three lectures) to replicate Experiment 1. The pilot study showed that subjects expect only inexperienced professors to have variable performance. Overall rating accuracy was lowest in the moderate variability condition and in the high experience condition. Accuracy component analyses (Cronbach, 1955) indicated that more variable performance was rated more accurately. Further, marginal evidence suggested that higher cognitive complexity is somewhat associated with greater differential elevation accuracy when experienced ratees have variable performance. Also, effort and chance attributions were strongest in the moderate experience condition. In Experiment 2, highly experienced ratees and those with moderate variability were rated more accurately than others. Cognitive complexity was not helpful to rating accuracy. In one case, it was associated with more underrating of highly experienced, highly variable ratees. General support was found for the contention that prototype expectations exist and affect rating accuracy. Very limited support was gained for cognitive complexity as a useful rater characteristic. In consistent experimental findings are discussed in terms of the salience of performance variability and cognitive processing of information. Implications for training raters to observe multiple performance dimensions and attend to the possible biasing affects of ratee characteristics are also discussed.

Product complexity has been studied as an important factor to decrease the cost and time of the development process. With this purpose, prior research has included the development of design complexity metrics as a method to assess and decrease complexity. Recent studies have also focused on the comparison of complexity metrics for the particular case of medical devices development (MDD). However, the major issue relevant to MDD has not been addressed; the relationship between FDA regulations and the device complexity is not clarified. Therefore, to increase MDD safety and decrease the time to market, we must understand the regulatory decision process and rules. In this paper, we investigate the relation between different complexity metrics and FDA’s decision time using a sample of 100 hip replacement devices. Bayesian network learning is used to explore in detail local relationships between different variables, both complexity measures and product variables. This relationship was found significant for the first two clusters of the analysis. However, for a third cluster it is speculated that FDA decision time does not depend solely upon the degree of medical device complexity. Company or organization relevant variables could be playing a greater role than just complexity. Additional questions are drawn based on the results that must be investigated.

The recent Additive manufacturing (AM) literature has primarily concentrated on exploring new avenues for improving the current technology and its applicability. It has also delved into research investments aimed at addressing the last remaining prediction challenges associated with current AM processes, particularly focusing to surface quality, accuracy, and internal composition. These limitations can often be mitigated though the application of post-processing techniques. Such techniques are often very costly both in time and monetary terms.When it comes to the impact that shape complexity has on post-fabrication costs for AM parts, a gap in the literature is apparent. In recent years, more attention has been devoted to researching a general shape complexity metric. It has been suggested in the literature to combine multiple of complexity metrics techniques, to reach more comprehensive model. This aspect has not received enough attention in previous works. In addition, the relationship between shape complexity and post-processing costs has not been assessed. And there are no predictive models for post-processing costs based on complexity.In this study, AM shapes for prototyping application are analysed. In this regard, previously established complexity metrics are used, together with expert’s assessments of post-processing costs, to create a model capable of predicting post-processing costs. This is achieved through a regression analysis using costs and complexity metrics values. The result of this research are two regression models, named 7 V and Vol/Sur Models, capable of predicting post-processing costs for AM parts produced through DMLS techniques with SS316L stainless steel powder. The accuracy of the two models is discussed.

In this paper, an attempt has been made to evaluate cognitive information complexity measure (CICM) terms of nine Weyuker properties. It has been found that all the nine properties have been satisfied by CICM and hence establishes cognitive information complexity measure based on information contained in the software as a robust and well-structured one

Cognitive complexity measures quantify human difficulty in understanding the source code based on cognitive informatics foundation. The discipline derives cognitive complexity on a basis of fundamental software factors i.e. inputs, outputs, and internal processing architecture. The invention of Cognitive Functional Size (CFS) stands out as the breakthrough to software complexity measures. Several subsequent researches have tried to enhance CFS to fully consider more factors, such as information contents in the form of identifiers and operators. However, these existing approaches quantify the factors separately without considering the relationships among them. This thesis presents an approach to integrating Granular Computing into the new measure called Structured Cognitive Information Measure or SCIM. The proposed measure unifies and re-organizes complexity factors analogous to human cognitive process. Empirical studies were conducted to evaluate the virtue of SCIM, including theoretical validation through nine Weyuker's properties. The universal applicability of granular computing concepts is also demonstrated. Additionally, the new inductive framework has been proposed to patch the holes of Weyuker’s properties, and used in the assessment of the cognitive complexity measures to analyze and guide directions for future improvement of the measures.

The design study herein analyzes the design complexity of high zoom ratio lens systems in the visible, SWIR, and LWIR spectrums with four zoom groups (two internally moving). The aforementioned 12.5x zoom lens systems have been designed for use in the Coast Guard for maritime safety, security, and stewardship. To begin our comparative design study, the most advantageous solutions for distinct power groupings were found using a first order solution finder tool. The results showed that solutions with a PNNP, PNPP, and NPNP power grouping with the aperture stop in the third or fourth group had the most potential. At the end of the design process, a comparison was done for the three different wavebands to analyze the relative design complexity. Design complexity metrics were as follows: element count, number of aspheric surfaces, system total track length, element diameter, and tolerance sensitivity.

To test the assertion by Foa, Mitchell, and Fiedler (1971) and by Mitchell (1970) that Fiedler's Least Preferred Co-worker scale measures cognitive complexity, questionnaires from 357 subjects were subjected to the complexity analysis of Foa, et al. (1971). The perceptions of high scorers were not more complex than those of low scorers. Self-ratings of effectiveness of performance of 88 leaders of small groups were correlated with the ratings of their 269 subordinates. Self-assessments of high scoring leaders were not more accurate than those of low scoring leaders. Results seem to question cognitive complexity as an interpretation of scores on the Least Preferred Co-worker scale. In a second analysis, the variance score of the questionnaire, another measure of cognitive complexity (Evans & Dermer, 1974), showed subjects with high variance held more complex perceptions of their least preferred co-workers and made more accurate assessments of their own performance than did subjects with low variance.

Complexity is developed to demonstrate feasible metrics for process obtaining objectives and quantifiable measurement, which may have numerous valuable applications in schedule and budget planning, cost estimation and optimal personnel task assignments a particular software. Also it aids the developer and practitioners in evaluating the software complexity due to its simplicity, which serves both as an analyzer and as a predicator in quantitative software engineering. In this research work, the Halstead complexity metrics were applied to three sorting algorithms and C-Sharp programming language was used to implement them. The results shows the variant performances of sorting algorithms and the best algorithm that can perform better in a defined system of application.

Cognitive informatics (CI), a multidisciplinary area of research tries to solve the common problems in the field of informatics, computer science, software engineering, mathematics, cognitive science, neurobiology, psychology, and physiology. Measurement in software engineering is also a core issue which is still striving for its standardization process. In recent years, several cognitive complexity measures based on CI have been proposed. However, each of them has their own advantages and disadvantages. This chapter presents a critical review on existing cognitive complexity measures. Furthermore, a comparative study based on some selected attributes has been presented.

The measurement of the quality attributes of software is a management approach to building quality software product. Testability is a measure of the capability of the software to be subjected to testing. It is a desirable quality attribute of software product because it ensures the reliability of software. This paper presents an empirical validation of coupling and cohesion metrics as indicators of the testability quality attributes of Object-Oriented (OO) software from High-Level Design (HLD) perspective. Open-source OO software samples are used for the empirical analysis and three test case complexity metrics are taken as the measure of the complexity of testing the software sample. The results of the empirical validation showed that the coupling metrics and some of the cohesion metrics investigated are good indicators of the testability of OO software design.

Quality of data models for data warehouse has significant effect on the quality of data warehouse. Complexity metrics play significant role in predicting quality attributes of a software artifact. Few researchers have proposed structural complexity metrics for the multidimensional data models for data warehouse which may act as objective indicators of the quality of these models. However, the metrics proposed earlier have not considered the structural complexity due to relationships among various elements present in these models. This paper proposes a complexity metric which considers structural complexity due to relationships among elements present in multidimensional models for data warehouse. The metric is proposed on the basis of Goal Question Metric approach. The practical usefulness of the proposed metric is proved by validating the metric using a practical framework proposed by Kaner. This preliminary validation suggests that the metric may be linked to the quality of the multidimensional models. The advantage of the metric is that it is available during early phase of software development life cycle. The metric will also help the developers to select quality data model among various semantically equivalent models.

73 male and 72 female college students completed several measures delineating aspects of the relationships they perceived between themselves and their social and physical environment, as well as cognitive complexity and field-independence measures. Correlational and cluster analysis showed complexity and field independence occurred within differing patterns of self-environment relations for men and women. Differing implications for the simultaneous development of verbal and non-verbal analytic cognitive styles in men and women were suggested by the findings. Results emphasized an ecological approach to cognitive styles as necessary for understanding the information processes underlying the styles themselves as well as their adjustive implications for men and women.

This article reports on the defintion and the measutement of the software complexity metrics of Halstead and McCabe for programs written in the functional programming language Miranda. An automated measurement of these metrics is described. In a case study, the correlation is established between the complexity metrics and the expert assessment of the readability of programs in Miranda, and compared with those for programs in Pascal.

Aspect-Oriented Software Development is a promising new software engineering paradigm. It promotes, in particular, improved separation of crosscutting concerns into single units called aspects. AspectJ, the most used aspect-oriented programming language, represents an extension of Java. In fact, existing objectoriented programming languages suffer from a serious limitation in modularizing adequately crosscutting concerns. Many concerns crosscut several classes in an object-oriented system. Moreover, several metrics have been proposed in order to assess object-oriented software quality attributes. However, these metrics do not cover the new abstractions and complexity dimensions introduced by the aspect paradigm. As a consequence, new metrics must be developed to assess aspectoriented systems quality attributes. Cohesion is considered as one of the most important software quality attributes. Cohesion refers to the degree of relatedness between members of a software component. We propose, in this paper, a new approach for aspect cohesion measurement based on dependencies analysis. We introduce several cohesion criteria taking into account aspects' features and capturing various dependencies between their members. We also propose a new aspect cohesion metric and compare it, using several case studies, to few existing aspect cohesion metrics.