Measure Of Cognitive Complexity Research Articles

An empirical study on software understandability and its dependence on code characteristics

ContextInsufficient code understandability makes software difficult to inspect and maintain and is a primary cause of software development cost. Several source code measures may be used to identify difficult-to-understand code, including well-known ones such as Lines of Code and McCabe’s Cyclomatic Complexity, and novel ones, such as Cognitive Complexity.ObjectiveWe investigate whether and to what extent source code measures, individually or together, are correlated with code understandability.MethodWe carried out an empirical study with students who were asked to carry out realistic maintenance tasks on methods from real-life Open Source Software projects. We collected several data items, including the time needed to correctly complete the maintenance tasks, which we used to quantify method understandability. We investigated the presence of correlations between the collected code measures and code understandability by using several Machine Learning techniques.ResultsWe obtained models of code understandability using one or two code measures. However, the obtained models are not very accurate, the average prediction error being around 30%.ConclusionsBased on our empirical study, it does not appear possible to build an understandability model based on structural code measures alone. Specifically, even the newly introduced Cognitive Complexity measure does not seem able to fulfill the promise of providing substantial improvements over existing measures, at least as far as code understandability prediction is concerned. It seems that, to obtain models of code understandability of acceptable accuracy, process measures should be used, possibly together with new source code measures that are better related to code understandability.

Cognitive complexity measures for educational texts: Empirical validation of linguistic parameters

The article presents a study conducted within the framework of discourse complexology - an integral scientific domain that has united linguists, cognitive scientists, psychologists and programmers dealing with the problems of discourse complexity. The issue of cognitive complexity of texts is one of the central issues in discourse complexology. The paper presents the results of the study aimed to identify and empirically validate a list of educational texts’ complexity predictors. The study aims to identify discriminant linguistic parameters sufficient to assess cognitive complexity of educational texts. We view text cognitive complexity as a construct, based on the amount of presented information and the success of reader-text interactions. The idea behind the research is that text cognitive complexity notably increases across middle and high schools. The research dataset comprises eight biology textbooks with the total size of 219,319 tokens. Metrics of text linguistic features were estimated with the help of automatic analyzer RuLingva (rulingva.kpfu.ru). Linguistic and statistical analysis confirmed the hypothesis that text syntactic and lexical parameters are discriminative enough to classify different levels of cognitive complexity of educational texts used in middle and high schools. Text parameters that manifest variance in cognitive complexity include lexical diversity (TTR); local argument overlap; abstractness index; number of polysyllabic words, Flesch-Kincaid Grade Level; number of nouns and number of adjectives per sentence. Empirical evidence indicates that the proposed approach outperforms existing methods of text complexity assessment. The research results can be implemented in the system of scientific and educational content expertise for Russian school textbooks. They can also be of some use in the development of educational resources and further research in the field of text complexity.

Linguistic Fingerprints and Ideological Fragmentation

The past decade of national security interventions designed to counter or offer alternatives to extremist narratives have had questionable efficacy. Too frequently, interventions have been overly concerned with ‘message dominance’, focusing on ‘what’ people think, at the expense of ‘how’ people think. Intervention strategies have fundamentally ignored the role of cognitive processing in extremist decision making. This is in spite of an increasing body of evidence which demonstrates that low cognitive complexity (black and white thinking) is a recurrent contributing factor to extremist decision making. In this article, Joshua Stewart introduces Integrative Complexity (IC), a psychological measure of cognitive complexity. Through a hypothetical futures scenario, he explores the wider potential of IC in meeting national security objectives and poses that even the most obdurate, black and white thinking can be leveraged towards positive behaviours.◼

Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance effort. Cognitive informatics is important in quantifying the degree of difficulty or the efforts made by developers to understand the source code. Several empirical studies were conducted in 2003 to assign cognitive weights to each possible basic control structure of software, and these cognitive weights are used by several researchers to evaluate the cognitive complexity of software systems. In this paper, an effort has been made to categorize the Control Flow Graphs (CFGs) nodes according to their node features. In our case, we extracted seven unique features from the program, and each unique feature was assigned an integer value that we evaluated through Cognitive Complexity Measures (CCMs). We then incorporated CCMs’ results as a node feature value in CFGs and generated the same based on the node connectivity for a graph. In order to obtain the feature representation of the graph, a node vector matrix is then created for the graph and passed to the Graph Convolutional Network (GCN). We prepared our data sets using GCN output and then built Deep Neural Network Defect Prediction (DNN-DP) and Convolutional Neural Network Defect Prediction (CNN-DP) models to predict software defects. The Python programming language is used, along with Keras and TensorFlow. Three hundred twenty Python programs were written by our talented UG and PG students, and all experiments were carried out during laboratory classes. Together with three skilled lab programmers, they compiled and ran each individual program and detected defect/no-defect programs before categorizing them into three different classes, namely Simple, Medium, and Complex programs. Accuracy, Receiver Operating Characteristics (ROC), Area Under Curve (AUC), F-measure, Precision and hyper-parameter tuning procedures are used to evaluate the approaches. The experimental results show that the proposed models outperformed state-of-the-art methods such as Nave Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) in all evaluation criteria.

Automatizing Software Cognitive Complexity Reduction

Software plays a central role in our life nowadays. We use it almost anywhere, at any time, and for everything: to browse the Internet, to check our emails, and even to access critical services such as health monitoring and banking. Hence, its reliability and general quality is critical. As software increases in complexity, developers spend more time fixing bugs or making code work rather than designing or writing new code. Thus, improving software understandability and maintainability would translate into an economic relief over the total cost of a project. Different cognitive complexity measures have been proposed to quantify the understandability of a piece of code and, therefore, its maintainability. However, the cognitive complexity metric provided by SonarSource and integrated in SonarCloud and SonarQube is quickly spreading in the software industry due to the popularity of these well-known static code tools for evaluating software quality. Despite SonarQube suggests to keep method’s cognitive complexity no greater than 15, reducing method’s complexity is challenging for a human programmer and there are no approaches to assist developers on this task. We model the cognitive complexity reduction of a method as an optimization problem where the search space contains all sequences of Extract Method refactoring opportunities. We then propose a novel approach that searches for feasible code extractions allowing developers to apply them, all in an automated way. This will allow software developers to make informed decisions while reducing the complexity of their code. We evaluated our approach over 10 open-source software projects and was able to fix 78% of the 1,050 existing cognitive complexity issues reported by SonarQube. We finally discuss the limitations of the proposed approach and provide interesting findings and guidelines for developers.

The Correlation among Cognitive Complexity Metrics in Algorithm Analysis

In the early stage of software development, design complexity metrics are considered as useful indicators of a software testing effort and quality attributes. However, existing works made great efforts in establishing standardized metrics to evaluate the complexity of software, but there have not been significant efforts in finding the correlations among the cognitive complexity metrics. To address this challenge, this paper reviewed cognitive complexity metrics which includes: Improved Cognitive Complexity Measure (ICCM), New Cognitive Complexity of Program (NCCoP) and Modified Cognitive Complexity Measure (MCCM). The metrics were employed to analyse some selected sorting algorithms implemented in a procedural C programming language. The relationships among the aforementioned metrics were calculated using the Pearson Correlation Coefficient Method. The results of the comparative examination of ICCM, NCCoP and MCCM revealed that ICCM had more responsive measurements and that there exists a strong relationship among the specified metrics. ICCM had the strongest significance among the considered metrics based on the efforts in comprehending the information contained in the sorting algorithm codes. The study contributed significantly to understanding and addressing the complexity emanating from software development.

Re-introducing Cognitive Complexity: A Meta-analysis and Agenda for Future Research

ABSTRACTCognitive complexity represents the extent to which individuals cognitively structure and process their environment in a multidimensional manner. Despite over 60 years of research, gaps remain in our understanding of the construct. In this meta-analytic review (k = 74, n = 18,973), we addressed a set of research questions regarding the nomological network surrounding cognitive complexity as well as the incremental validity and relative weight of cognitive complexity over traditional predictors of job outcomes. Cognitive complexity exhibited moderate correlations with constructs like cognitive ability (ρ = .19) and task complexity (ρ = − .18), but performed poorly in relative weights and incremental validity analyses. Given our findings, we suggest provide avenues for future research to explore and present a measure of cognitive complexity to advance the literature.

Увеличение интегративной сложности у осужденных террористов в Индонезии

Located in Indonesia, the aim of the present study was to test the dynamics of integrative complexity of convicted terrorists in a series of dialogs set by the researchers. It was expected that if the meeting containing ideas related to humanity, peace, and intergroup harmony could be able to make the attendees stay until the end, cognitive complexity of the last meeting would be higher than the first meeting. Fifty nine statements were randomly collected from thirty eight convicted terrorists who participated in the meeting. The statements, then, were scored based on the level of cognitive complexity. A measure of cognitive complexity showed significant (Wilks’ Lambda = .748, F (3, 56) = 6.30, p < .001, partial eta2 = .252) increase during the process of the four meetings; compared to meeting 1, the level of integrative complexity in meeting 2, 3, and 4 were consistently higher. The findings indicate the possibility to increase the level of integrative complexity of the members of terrorist groups in a series of dialogs by highlighting the discussion about humanity and peace.

Content and alignment of state writing standards and assessments as predictors of student writing achievement: an analysis of 2007 National Assessment of Educational Progress data

We examined the degree to which content of states’ writing standards and assessments (using measures of content range, frequency, balance, and cognitive complexity) and their alignment were related to student writing achievement on the 2007 National Assessment of Educational Progress (NAEP), while controlling for student, school, and state characteristics. We found student demographic characteristics had the largest effect on between-state differences in writing performance, followed by state policy-related variables, then state and school covariates. States with writing tests that exhibited greater alignment with the NAEP writing assessment demonstrated significantly higher writing scores. We discuss plausible implications of these findings.

The Centrality of the Response Process in Construct Validity: An Illustration via the Rorschach Space Response

ABSTRACTRecently, psychologists have emphasized the response process—that is, the psychological operations and behaviors that lead to test scores—when designing psychological tests, interpreting their results, and refining their validity. To illustrate the centrality of the response process in construct validity and test interpretation, we provide a historical, conceptual, and empirical review of the main uses of the background white space of the Rorschach cards, called space reversal (SR) and space integration (SI) in the Rorschach Performance Assessment System. We show how SR and SI's unique response processes result in different interpretations, and that reviewing their literatures with these distinct interpretations in mind produces the expected patterns of convergent and discriminant validity. That is, SR was uniquely related to measures of oppositionality; SI was uniquely related to measures of cognitive complexity; and both SR and SI were related to measures of creativity. Our review further suggests that the Comprehensive System use of a single space code for all uses of white space likely led to its lack of meta-analytic support as a measure of oppositionality (Mihura, Meyer, Dumitrascu, & Bombel, 2013). We close by discussing the use of the response process to improve test interpretation, develop better measures, and advance the design of research.

Promoting Cognitive Development Through Field Education

ABSTRACT This article reports results from a study examining the effects of field education on cognitive development. BSW students enrolled in either a semester-long practicum/field seminar or prepracticum courses completed pretest and posttest measures of cognitive complexity to assess cognitive development. Results indicated that field practicum students made significantly higher gains in cognitive complexity than prepracticum students even when controlling for age and ethnicity. The research suggests that field education, that is, working with actual clients under the supervision of more senior members of the profession, and participating in a field seminar course with other social work students have important implications for enhancing cognitive development.

Is a ‘Complex’ Task Really Complex? Validating the Assumption of Cognitive Task Complexity

In research on task‐based learning and teaching, it has traditionally been assumed that differing degrees of cognitive task complexity can be inferred through task design and/or observations of differing qualities in linguistic production elicited by second language (L2) communication tasks. Without validating this assumption, however, it is unclear at best whether the designed or inferred difference in complexity, the key independent variable, is realized as intended. Accordingly, this study adopted diverse methods from cognitive psychology for independently measuring cognitive task complexity, including: dual‐task methodology, time estimation, and self‐rating. Fifty‐three English‐L2 speakers in Japan, representing distinct proficiency levels, narrated 4 picture sequences, each containing differing numbers of characters. While performing the primary story‐telling task, participants simultaneously completed a secondary task of reacting to a color change. After each story‐telling, they also estimated their time‐on‐task and rated their perceptions of task difficulty and mental effort. Results revealed that only large differences in the task design feature (i.e., number of elements) were detectable in terms of independent measures of cognitive complexity. This finding underscores the importance of validating the assumptions about the relationship between task design and its putative effects on cognitive complexity. Findings also indicated a potential interaction among proficiency, task designs, and measures of cognitive load.

Integrating the Differentiated: A Review of the Personal Construct Approach to Cognitive Complexity

This article reviews personal construct psychology (PCP) research on cognitive complexity. It examines conceptual foundations, measures of cognitive complexity, and a large body of empirical findings. It identifies several ambiguities in the conceptualization of the two components of cognitive complexity: differentiation and integration. These ambiguities lead to inconsistent interpretations of indexes proposed for their measurement and consequently to an inconsistent interpretation of empirical findings. The review of empirical studies identifies many valuable findings that are, however, quite fragmented. After reviewing the relevant cognitive complexity literature, suggestions for future research are discussed. In accordance with PCP's organization corollary, two different types of integration that have different relationships to the component of differentiation are identified that enable researchers to consistently distinguish various types of construing in terms of cognitive complexity and to formulate hypotheses that are open to empirical testing.

Measurement of Complexity and Comprehension of a Program Through a Cognitive Approach

The inherent complexity of the software systems creates problems in the software engineering industry. Numerous techniques have been designed to comprehend the fundamental characteristics of software systems. To understand the software, it is necessary to know about the complexity level of the source code. Cognitive informatics perform an important role for better understanding the complexity of the software. These informatics also facilitate researchers to understand the behavior of the source code, internal as well as the control structure. This paper presents a new cognitive complexity measure (C2M) for measuring the complexity of the software system and it is tested on 50 ‘C’ programs. The proposed C2M measure was also validated with the help of Weyuker property; eight out of nine properties were satisfied by the proposed measure. The performance of the proposed measure was also compared with other existing cognitive complexity measures. The test data was distributed among 10 undergraduate students of our institute and they were asked to understand the source code. The time taken by the individual student was recorded and the meantime of the recorded data from students was considered as the actual time required to understand the program. It was further correlated with the estimated time, which was calculated through C2M measure. From the experimental results, it was observed that proposed measure provided better quality results.

A longitudinal examination of re-employment quality on internalizing symptoms and job-search intentions.

Underemployed workers-those receiving less pay, working fewer hours, or using fewer skills than they would prefer-appear to experience negative mental health outcomes similar to the unemployed. Prior cross-sectional research provides mixed empirical evidence for this conclusion, however. The current study sought to clarify the impact of underemployment longitudinally, assessing mental health 5 times over 8 months after job loss. In addition to the commonly used indicators of underemployment, we designed a measure of cognitive complexity using the Occupational Information Network (O*NET), an extensive government database used to organize and categorize occupational information. Replicating past research, we found concurrent associations between all indexes of reemployment job quality and internalizing symptoms in the period immediately after reemployment. However, when controlling for quality of prior employment, all indicators except our measure for cognitive complexity became nonsignificant. As participants transitioned from unemployment to reemployment, only reductions in cognitive complexity were associated with sustained general internalizing symptoms. We also found that although changes in cognitive complexity had an immediate impact on the well-being of the recently reemployed, only the number of available weekly hours (full-time vs. part-time status) was relevant 6 to 12 weeks later. Our longitudinal model thus provides significant nuance to the current understanding of underemployment and mental health.

The Role of Communication Complexity in Adaptive Contextualization

Research problem: Adding contextual information to a core message has been shown to be critical in improving communication quality, especially in computer mediated communication. This paper models how people contextualize messages in the face of changing communication complexity. Research question: Can changes in communication complexity that occur during the communication process explain and predict contextualization? Literature review: Theories of human communication and studies of computer supported collaboration suggest that communication complexity reflects potentially problematic conditions resulting from 1) the difference in perspective and context held by the collaborators; 2) the incompatibility between the message representation and the way it is interpreted and used by the receiver; and 3) the intensity of information exchanged between communicators. We use this definition as a basis of for developing a measure of cognitive communication complexity. The literature further suggests that higher communication complexity induces higher contextualization. Methodology: First, we conducted a pilot study to develop and validate measures of communication complexity. Second, we conducted a laboratory experiment, in which 258 participants working in pairs collaborated on a sixteen-step assembly task. They used a tailored system that structured each message as core (the essence of the message) and context (additional information that explains the core and the sender's perspective). We used unbalanced panel data analysis to examine the repeated measures of contextualization and communication complexity associated with each step of the task. Results and discussion: We found that collaborators respond to changes in communication complexity at the expense of higher collaborative effort. We offer a cost-benefit framework in which, at the step level, people contextualize to reduce the communication complexity, and at the task level, they additionally consider the impact of contextualization on task performance. The main limitation of this study was the need to structure the communication between collaborators, to control and measure contextualization. Future research can adapt and extend our measure of communication complexity to less structured communication.

THE IMPACT OF COGNITIVE COMPLEXITY ON FEEDBACK EFFICACY DURING ONLINE VERSUS FACE-TO-FACE INTERACTIVE TASKS

Informed by the cognition hypothesis (Robinson, 2011), recent studies indicate that more cognitively complex tasks can result in better incorporation of feedback during interaction and, as a consequence, more learning. It is not known, however, how task complexity and feedback work together in computerized environments. The present study addressed this gap by investigating how cognitive complexity in face-to-face (FTF) versus computer-mediated communication (CMC) environments mediates the efficacy of recasts in promoting second language development. Eighty-four adult learners of Spanish as a foreign language at a mid-Atlantic university were randomly assigned to a control group or one of four experimental groups. The experimental groups engaged in one-on-one interaction and received recasts on the Spanish past subjunctive but differed according to (a) whether or not they had to reflect on another person’s intentional reasons during the task and (b) whether they interacted in FTF or CMC environments. Learning was measured with two production tasks and a multiple-choice receptive test in a Pretest-Posttest 1-Posttest 2 design. Results revealed that in the FTF mode, performing the cognitively complex task while receiving recasts led to the most learning. In the CMC mode, the cognitively complex task + recasts was not effective. Instead, the cognitively simple task led to the most development in CMC. The study also found that judgments of time on task were the only independent measure of cognitive complexity that held across mode.

Subsymmetries Predict Auditory and Visual Pattern Complexity

A mathematical measure of pattern complexity based on subsymmetries possessed by the pattern, previously shown to correlate highly with empirically derived measures of cognitive complexity in the visual domain, is found to also correlate significantly with empirically derived complexity measures of perception and production of auditory temporal and musical rhythmic patterns. Not only does the subsymmetry measure correlate highly with the difficulty of reproducing the rhythms by tapping after listening to them, but also the empirical measures exhibit similar behavior, for both the visual and auditory patterns, as a function of the relative number of subsymmetries present in the patterns.

An Exploration of Language-Based Multiplicity in Fluent Bilinguals

This study used projective tests to explore the language-based shifts in aspects of personality observed in fluent bilinguals. The Rorschach Comprehensive System (RCS) and the Thematic Apperception Test (TAT) were administered in Spanish and in English to 26 fluent Spanish/English bilinguals, and protocols were compared based on measures of affect, self-perception, cognitive complexity, and object relations. No significant differences were found between language protocols for most measures, nor did these measures relate to age of second language learning, relative use of English and Spanish, or participation in therapy. However, cognitive effort was significantly greater on English RCS protocols, and a strong correlation between level of acculturation and freedom of expression in English was found. Further exploration of the data revealed very important differences between most subjects’ Spanish and English protocols. Most notably, in 73% of the cases, RCS protocols differed so extensively by language that different key variable for cluster interpretation and diagnosis were indicated, and 42% of subjects’ modes of decision making (EB style) varied according to language. These findings are remarkable because these key variables are believed to measure constant, dominant and trait-like features of an individual’s psychology. The findings underscored the need for norms based on this population, and development of culturally-based constructs for interpretation of the RCS.

Measures of Cognitive Complexity and Its Development in Chinese Adolescents

There are various controversies over the measurement of cognitive complexity and few studies concerning its development in adolescents, especially in China. Based on a sample of 234 Chinese adolescents from Grades 7 through 11, the present study first compared 2 instruments and 12 corresponding indices of cognitive complexity—11 traditional and new forms of cognitive complexity scores on the Repgrid, and 1 score on the Role Category Questionnaire (RCQ)—then adopted the most appropriate tool to investigate the development of cognitive complexity. The results showed that the RCQ may have relatively more construct validity than the Repgrid in terms of the assessment of adolescents’ cognitive complexity. Using the RCQ, it was found that adolescents of different grades, gender, peer status, and academic achievement had different levels of cognitive complexity.