EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.

Using social media and personality traits to assess software developers' emotional polarity.

Although human factors (e.g., cognitive functions, behaviors and skills, human error models, etc.) are key elements to improve software development productivity and quality, the role of software developers' emotions and their personality traits in software engineering still needs to be studied. A major difficulty is in assessing developers' emotions, leading to the classic problem of having difficulties understanding what cannot be easily measured. Existing approaches to infer emotions, such as facial expressions, self-assessed surveys, and biometric sensors, imply considerable intrusiveness on developers and tend to be used only during normal working periods. This article proposes to assess the feasibility of using social media posts (e.g., developers' posts on Twitter) to accurately determine the polarity of emotions of software developers over extended periods in a non-intrusive manner, allowing the identification of potentially abnormal periods of negative or positive sentiments of developers that may affect software development productivity or software quality. Our results suggested that Twitter data can serve as a valid source for accurately inferring the polarity of emotions. We evaluated 31 combinations of unsupervised lexicon-based techniques using a dataset with 79,029 public posts from Twitter from sixteen software developers, achieving a macro F1-Score of 0.745 and 76.8% of accuracy with the ensemble comprised of SentiStrength, Sentilex-PT, and LIWC2015_PT lexicons. Among other results, we found a statistically significant difference in tweets' polarities posted during working and non-working periods for 31.25% of the participants, suggesting that emotional polarity monitoring outside working hours could also be relevant. We also assessed the Big Five personality traits of the developers and preliminarily used them to ponder the polarities inferences. In this context, Openness, Conscientiousness, and Extraversion were frequently related to neutral and positive posts, while Neuroticism is associated with negative posts. Our results show that the proposed approach is accurate enough to constitute a simple and non-intrusive alternative to existing methods. Tools using this approach can be applied in real software development environments to support software team workers in making decisions to improve the software development process.

How Reliable Are Ultra-Short-Term HRV Measurements during Cognitively Demanding Tasks?

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements' reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland-Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

Software Bug Detection Causes a Shift From Bottom-Up to Top-Down Effective Connectivity Involving the Insula Within the Error-Monitoring Network.

The neural correlates of software programming skills have been the target of an increasing number of studies in the past few years. Those studies focused on error-monitoring during software code inspection. Others have studied task-related cognitive load as measured by distinct neurophysiological measures. Most studies addressed only syntax errors (shallow level of code monitoring). However, a recent functional MRI (fMRI) study suggested a pivotal role of the insula during error-monitoring when challenging deep-level analysis of code inspection was required. This raised the hypothesis that the insula is causally involved in deep error-monitoring. To confirm this hypothesis, we carried out a new fMRI study where participants performed a deep source-code comprehension task that included error-monitoring to detect bugs in the code. The generality of our paradigm was enhanced by comparison with a variety of tasks related to text reading and bugless source-code understanding. Healthy adult programmers (N = 21) participated in this 3T fMRI experiment. The activation maps evoked by error-related events confirmed significant activations in the insula [p(Bonferroni) < 0.05]. Importantly, a posterior-to-anterior causality shift was observed concerning the role of the insula: in the absence of error, causal directions were mainly bottom-up, whereas, in their presence, the strong causal top-down effects from frontal regions, in particular, the anterior cingulate cortex was observed.

On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement.

Complexity is the key element of software quality. This article investigates the problem of measuring code complexity and discusses the results of a controlled experiment to compare different views and methods to measure code complexity. Participants (27 programmers) were asked to read and (try to) understand a set of programs, while the complexity of such programs is assessed through different methods and perspectives: (a) classic code complexity metrics such as McCabe and Halstead metrics, (b) cognitive complexity metrics based on scored code constructs, (c) cognitive complexity metrics from state-of-the-art tools such as SonarQube, (d) human-centered metrics relying on the direct assessment of programmers' behavioral features (e.g., reading time, and revisits) using eye tracking, and (e) cognitive load/mental effort assessed using electroencephalography (EEG). The human-centered perspective was complemented by the subjective evaluation of participants on the mental effort required to understand the programs using the NASA Task Load Index (TLX). Additionally, the evaluation of the code complexity is measured at both the program level and, whenever possible, at the very low level of code constructs/code regions, to identify the actual code elements and the code context that may trigger a complexity surge in the programmers' perception of code comprehension difficulty. The programmers' cognitive load measured using EEG was used as a reference to evaluate how the different metrics can express the (human) difficulty in comprehending the code. Extensive experimental results show that popular metrics such as V(g) and the complexity metric from SonarSource tools deviate considerably from the programmers' perception of code complexity and often do not show the expected monotonic behavior. The article summarizes the findings in a set of guidelines to improve existing code complexity metrics, particularly state-of-the-art metrics such as cognitive complexity from SonarSource tools.

Reading and Calculation Neural Systems and Their Weighted Adaptive Use for Programming Skills.

Software programming is a modern activity that poses strong challenges to the human brain. The neural mechanisms that support this novel cognitive faculty are still unknown. On the other hand, reading and calculation abilities represent slightly less recent human activities, in which neural correlates are relatively well understood. We hypothesize that calculus and reading brain networks provide joint underpinnings with distinctly weighted contributions which concern programming tasks, in particular concerning error identification. Based on a meta-analysis of the core regions involved in both reading and math and recent experimental evidence on the neural basis of programming tasks, we provide a theoretical account that integrates the role of these networks in program understanding. In this connectivity-based framework, error-monitoring processing regions in the frontal cortex influence the insula, which is a pivotal hub within the salience network, leading into efficient causal modulation of parietal networks involved in reading and mathematical operations. The core role of the anterior insula and anterior midcingulate cortex is illuminated by their relation to performance in error processing and novelty. The larger similarity that we observed between the networks underlying calculus and programming skills does not exclude a more limited but clear overlap with the reading network, albeit with differences in hemispheric lateralization when compared with prose reading. Future work should further elucidate whether other features of computer program understanding also use distinct weights of phylogenetically "older systems" for this recent human activity, based on the adjusting influence of fronto-insular networks. By unraveling the neural correlates of program understanding and bug detection, this work provides a framework to understand error monitoring in this novel complex faculty.

Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers' Cognitive Load?

An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers' cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers' cognitive load associated with the comprehension of code with different complexity levels. Therefore, a controlled experiment involving 26 programmers was carried. We found that features related to Theta, Alpha, and Beta brain waves have the highest discriminative power, allowing the identification of code lines and demanding higher mental effort. The EEG results reveal evidence of mental effort saturation as code complexity increases. Conversely, the classic software complexity metrics do not accurately represent the mental effort involved in code comprehension. Finally, EEG is proposed as a reference, in particular, the combination of EEG with eye tracking information allows for an accurate identification of code lines that correspond to peaks of cognitive load, providing a reference to help in the future evaluation of the space and time accuracy of programmers' cognitive state monitored using wearable devices compatible with software development activities.

The role of the insula in intuitive expert bug detection in computer code: an fMRI study.

Software programming is a complex and relatively recent human activity, involving the integration of mathematical, recursive thinking and language processing. The neural correlates of this recent human activity are still poorly understood. Error monitoring during this type of task, requiring the integration of language, logical symbol manipulation and other mathematical skills, is particularly challenging. We therefore aimed to investigate the neural correlates of decision-making during source code understanding and mental manipulation in professional participants with high expertise. The present fMRI study directly addressed error monitoring during source code comprehension, expert bug detection and decision-making. We used C code, which triggers the same sort of processing irrespective of the native language of the programmer. We discovered a distinct role for the insula in bug monitoring and detection and a novel connectivity pattern that goes beyond the expected activation pattern evoked by source code understanding in semantic language and mathematical processing regions. Importantly, insula activity levels were critically related to the quality of error detection, involving intuition, as signalled by reported initial bug suspicion, prior to final decision and bug detection. Activity in this salience network (SN) region evoked by bug suspicion was predictive of bug detection precision, suggesting that it encodes the quality of the behavioral evidence. Connectivity analysis provided evidence for top-down circuit "reutilization" stemming from anterior cingulate cortex (BA32), a core region in the SN that evolved for complex error monitoring such as required for this type of recent human activity. Cingulate (BA32) and anterolateral (BA10) frontal regions causally modulated decision processes in the insula, which in turn was related to activity of math processing regions in early parietal cortex. In other words, earlier brain regions used during evolution for other functions seem to be reutilized in a top-down manner for a new complex function, in an analogous manner as described for other cultural creations such as reading and literacy.

Data and knowledge management in cross-Omics research projects.

Cross-Omics studies aimed at characterizing a specific phenotype on multiple levels are entering the -scientific literature, and merging e.g. transcriptomics and proteomics data clearly promises to improve Omics data interpretation. Also for Systems Biology the integration of multi-level Omics profiles (also across species) is considered as central element. Due to the complexity of each specific Omics technique, specialization of experimental and bioinformatics research groups have become necessary, in turn demanding collaborative efforts for effectively implementing cross-Omics. This setting imposes specific emphasis on data sharing platforms for Omics data integration and cross-Omics data analysis and interpretation. Here we describe a software concept and methodology fostering Omics data sharing in a distributed team setting which next to the data management component also provides hypothesis generation via inference, semantic search, and community functions. Investigators are supported in data workflow management and interpretation, supporting the transition from a collection of heterogeneous Omics profiles into an integrated body of knowledge.