Lucian Gonçales, Kleinner Farias, PPGCA, University of Vale do Rio dos Sinos (Unisinos) São Leopoldo, RS, Brazil lucianjosegoncales@gmail.com, kleinnerfarias@unisinos.br
Bruno C. da Silva California Polytechnic State University, Cal Poly San Luis Obispo, CA, USA
| ID | Reference |
|---|---|
| [S01] | N. Peitek, J. Siegmund, S. Apel, C. Kästner, C. Parnin, A. Bethmann, T. Leich, G. Saake, & A. Brechmann. A Look into Programmers' Heads. IEEE Transactions on Software Engineering, 2018. |
| [S02] | M. Zimoch, R. Pryss, J. Schobel, & M. Reichert. Eye Tracking Experiments on Process Model Comprehension: Lessons Learned. In Ent., Business-Process and Inf. Systems Modeling. BPMDS 2017. Lec. Notes in Business Inf. Proc., v 287. |
| [S03] | N. Nourbakhsh, Y. Wang, & F. Chen. GSR and blink features for cognitive load classification. In IFIP Conf. on HCI, 2013, pp. 159--166. |
| [S04] | I. Crk, T. Kluthe, & A. Stefik. Understanding Programming Expertise: An Empirical Study of Phasic Brain Wave Changes. ACM TOCHI. 23, 1, 2015. |
| [S05] | S. Fakhoury, Y. Ma, V. Arnaoudova, & O. Adesope The Effect of Poor Source Code Lexicon and Readability on Developers' Cognitive Load. ICPC, 2018. |
| [S06] | T. Fritz & S. C. Müller, Leveraging Biometric Data to Boost Software Developer Productivity. SANER, 2016, pp. 66--77. |
| [S07] | M. Borys, M. Plechawska-Wójcik, M. Wawrzyk & K. Wesołowska. Classifying Cognitive Workload Using Eye Activity and EEG Features in Arithmetic Tasks. Information and Software Technology, 2017, pp. 90--105. |
| [S08] | S. C. Müller & T. Fritz, Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress. ICSE, 2015, pp. 688--699. |
| [S09] | I. Crk & T. Kluthe, Assessing the contribution of the individual alpha frequency (IAF) in an EEG-based study of program comprehension. EMBC, 2016, pp. 4601--4604. |
| [S10] | D. Girardi, F. Lanubile, N. Novielli, & D. Fucci. 2018. Sensing developers' emotions: the design of a replicated experiment. SEmotion, 2018, pp. 51--54. |
| [S11] | R. Petrusel, J. Mendling, & H. A. Reijers. Task-specific visual cues for improving process model understanding. Information and Software Technology, v. 79, 2016, pp. 63--78. |
| [S12] | R. K. Minas, R. Kazman, & E. Tempero. Neurophysiological Impact of Software design processes on software developers. In Int. Conf. on Augmented Cognition, 2017, pp. 56--64. |
| [S13] | R. Petrusel, J. Mendling & H. A. Reijers. How visual cognition influences process model comprehension. Decision Support Systems, V. 96, 2017, pp. 1--16. |
| [S14] | M. Tallon, M. Winter, R. Pryss, K. Rakoczy, M. Reichert, M. W. Greenlee, & U. Frick. Comprehension of business process models: Insight into cognitive strategies via eye tracking. Expert Systems with Applications, V. 136, 2019, pp. 145--158. |
| [S15] | N. Peitek, S. Apel, A. Brechmann, C. Parnin, & J. Siegmund. CodersMUSE: multi-modal data exploration of program-comprehension experiments. ICPC, 2019, pp. 126--129. |
| [S16] | T. Baum, K. Schneider, & A. Bacchelli. Associating working memory capacity and code change ordering with code review performance. Empirical Software Engineering, 2019, v. 24, n. 4, pp. 1762--1798. |
| [S17] | S. Lee, A., Matteson, D. Hooshyar, S. Kim, J. Jung, G. Nam, & H. Lim. Comparing programming language comprehension between novice and expert programmers using EEG analysis. BIBE, 2016, pp. 350--355. |
| [S18] | K. Kevic, B. M. Walters, T. R. Shaffer, B. Sharif, D. C. Shepherd, & T. Fritz. Tracing software developers' eyes and interactions for change tasks. ESEC/FSE, 2015, pp. 202--213. |
| [S19] | M. Züger & T. Fritz. Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors. CHI. 2015, pp. 2981--2990. |
| [S20] | N. Peitek, J. Siegmund, C. Parnin, S. Apel, & A. Brechmann. Toward conjoint analysis of simultaneous eye-tracking and fMRI data for program-comprehension studies. EMIP, 2018. |
| [S21] | M. Behroozi & C. Parnin. Can we predict stressful technical interview settings through eye-tracking?. EMIP, 2018. |
| [S22] | J. Siegmund, N. Peitek, C. Parnin, S. Apel, J. Hofmeister, C. Kästner, A. Begel, A. Bethmann, & A. Brechmann, Measuring neural efficiency of program comprehension. In ESEC/FSE, 2017, pp. 140--150. |
| [S23] | J. S. Molléri, I. Nurdiani, F. Fotrousi, & K. Petersen. Experiences of studying Attention through EEG in the Context of Review Tasks.EASE, 2019, pp. 313--318. |
| [S24] | A. Duraisingam, R. Palaniappan, & S. Andrews. Cognitive task difficulty analysis using EEG and data mining. ICEDSS, 2017, pp. 52--57. |
| [S25] | D. Fucci, D. Girardi, N. Novielli, L. Quaranta, and F. Lanubile. A replication study on code comprehension and expertise using lightweight biometric sensors. In ICPC, 2019. |
| [S26] | J. Castelhano, I. C. Duarte, C. Ferreira, J. Duraes, H. Madeira, & M. Castelo-Branco. The role of the insula in intuitive expert bug detection in computer code: an fMRI study. Brain Imaging and Behavior, V. 13, I. 3, pp. 623--637. |
| [S27] | K. Kevic, B.M. Walters, T.R. Shaffer, B. Sharif, D.C. Shepherd, & T. Fritz Eye gaze and interaction contexts for change tasks – Observations and potential. JSS, V. 128, 2017, pp. 252--266. |
| [S28] | M. Konopka. Combining eye tracking with navigation paths for identification of cross-language code dependencies. ESEC/FSE, 2015, pp. 1057--1059 |
| [S29] | S. C. Müller & T. Fritz. Using (bio) metrics to predict code quality online. ICSE. 2016, pp. 452--463. |
| [S30] | S. C. Müller Measuring software developers' perceived difficulty with biometric sensors. ICPC, 2015, pp. 887--890. |
| [S31] | N. Peitek, J. Siegmund, C. Parnin, S. Apel, & A. Brechmann Beyond gaze: preliminary analysis of pupil dilation and blink rates in an fMRI study of program comprehension. In EMIP, 2018. |
| [S32] | M. Züger & T. Fritz Sensing and supporting software developers' focus. In ICPC, 2018. |
| [S33] | S. Lee, D. Hooshyar, H. Ji, K. Nam, & H. Lim. Mining biometric data to predict programmer expertise and task difficulty., Cluster Computing, 2017, pp. 1--11. |
| [S34] | R. Couceiro, G. Duarte, J. Durães, J. Castelhano, C. Duarte, C. Teixeira, M. C. Branco, P. Carvalho, & H. Madeira Biofeedback augmented software engineering: monitoring of programmers' mental effort. ICSE: NIER, 2019, pp. 37--40. |
| [S35] | T. Ishida, & H. Uwano Synchronized analysis of eye movement and EEG during program comprehension. EMIP, 2019, pp. 26--32. |
| [S36] | M. P. Uysal. Towards the use of a novel method: The first experiences on measuring the cognitive load of learned programming skills. Turkish Online Journal of Distance Education, v.14, n. 1, 2013, pp. 166--184. |
| [S37] | T. Fritz, A. Begel, S. C. Müller, S. Yigit-Elliott, & M. Züger. Using psycho-physiological measures to assess task difficulty in software development. ICSE, 2014, pp. 402--413. |
| [S38] | F. Schaule, J. O. Johanssen, B. Bruegge, & V. Loftness. Employing Consumer Wearables to Detect Office Workers' Cognitive Load for Interruption Management. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Tech., v. 2, n. 1, 2018, pp. 32:1--32:20. |
| [S39] | M. Züger, S. C. Müller, A. N. Meyer, & T. Fritz Sensing interruptibility in the office: A field study on the use of biometric and computer interaction sensors. CHI, 2018, pp. 591. |
| [S40] | Y. Huang, X. Liu, R. Krueger, T. Santander, X. Hu, K. Leach, & W. Weimer Distilling neural representations of data structure manipulation using fMRI and fNIRS. ICSE, 2019, pp. 396--407. |
| [S41] | M. V. Kosti, K. Georgiadis, D. A. Adamos, N. Laskaris, D. Spinellis, & L. Angelis Towards an affordable brain computer interface for the assessment of programmers' mental workload. Int. Journal of HCI, 2018, v. 115, pp. 52--66. |
| [S42] | M. Behroozi, A. Lui, I. Moore, D. Ford, & C. Parnin. Dazed: measuring the cognitive load of solving technical interview problems at the whiteboard. ICSE: NIER, 2018, pp. 93--96. |
| [S43] | J. Siegmund, C. Kästner, S. Apel, C. Parnin, A. Bethmann, T. Leich, G. Saake, & A. Brechmann Understanding understanding source code with functional magnetic resonance imaging. In ICSE, 2014, pp. 378--389. |
| [S44] | M. K. C. Yeh, D. Gopstein, Y. Yan, & Y. Zhuang. Detecting and comparing brain activity in short program comprehension using EEG. IEEE Front. in Education Conf., 2017, pp. 1--50. |
| [S45] | B. Floyd, T. Santander, & W. Weimer. Decoding the representation of code in the brain: an fMRI study of code review and expertise. ICSE, 2017, pp. 175--186. |
| [S46] | N. Peitek, J. Siegmund, C. Parnin, S. Apel, J. C. Hofmeister, A. Brechmann Simultaneous measurement of program comprehension with fMRI and eye tracking: a case study. ESEM, 2018. |
| [S47] | B. Sharif, & J. I. Maletic. An eye tracking study on camelcase and under_score identifier styles. ICPC, 2010, pp. 196--205. |
| [S48] | Y. Ikutani & H. Uwano Brain activity measurement during program comprehension with NIRS SNPD, 2014, pp. 1--6. |
| [S49] | J. Duraes, H. Madeira, J. Castelhano, C. Duarte, & M. C. Branco WAP: Understanding the Brain at Software Debugging ISSRE, 2016, pp. 87--92. |
| [S50] | T. Nakagawa, Y. Kamei, H. Uwano, A. Monden, K. Matsumoto, D. M. & German Quantifying programmers' mental workload during program comprehension based on cerebral blood flow measurement: a controlled experiment. ICSE, 2014, pp. 448-451. |
| [S51] | S. Radevski, H. Hata & K. Matsumoto Real-time monitoring of neural state in assessing and improving software developers' productivity. CHASE, 2015, pp. 93--96. |
| [S52] | A. Yamamoto, H. Uwano, & Y. Ikutani. Programmer's electroencephalogram who found implementation strategy. CHASE, 2015, pp. 93--96. ACIT-CSII-BCD, 2016, pp. 164-168. |
| [S53] | C. Parnin. Subvocalization-toward hearing the inner thoughts of developers. ICPC, 2011, pp. 197--200. |
| [S54] | J. Siegmund, A. Brechmann, S. Apel, C. Kästner, J. Liebig, T. Leich, & G. Saake. Toward measuring program comprehension with functional magnetic resonance imaging. ESEC/FSE, 2012, pp. 1-4. |
| [S55] | R. Ikramov, V. Ivanov, S. Masyagin, R. Shakirov, I. Sirazidtinov, G. Succi, & O. Zufarova. Initial evaluation of the brain activity under different software development situations. SEKE, 2018. |
| [S56] | S. Fakhoury, D. Roy, Y. Ma, V. Arnaoudova, & O. Adesope. Measuring the impact of lexical and structural inconsistencies on developers’ cognitive load during bug localization. Emp. Soft. Engineering, 1-39, 2019. |
| [S57] | R. Krueger, Y. Huang, X. Liu, T. Santander, W. Weimer, & K. Leach. Neurological Divide: An fMRI Study of Prose and Code Writing., ICSE, 2020. |
| [S58] | R. Palaniappan, A. Duraisingam, N. Chinnaiah, & M. Murugappan. Predicting Java Computer Programming Task Difficulty Levels Using EEG for Educational Environments. HCI, 2019, pp. 446--460. |
| [S59] | R. Couceiro, G. Duarte, J. Durães, J. Castelhano, C. Duarte, C. Teixeira, M. C. Branco, P. Carvalho & H. Madeira. Pupillography as Indicator of Programmers' Mental Effort and Cognitive Overload. DSN, 2019, pp. 638--644. |
| [S60] | J. Medeiros, R. Couceiro, J. Castelhano, M. Castelo Branco, G. Duarte, C. Duarte, J. Durães, H. Madeira, P. Carvalho, C. Teixeira. Software code complexity assessment using EEG features. International Conference of the IEEE EMBC, 2019, pp. 1413--1416. |
| [S61] | R. Couceiro, R. Barbosa, J. Duráes, G. Duarte, J. Castelhano, C. Duarte, C. Teixeira, N. Laranjeiro, J. Medeiros, P. Carvalho, H. Madeira. Spotting Problematic Code Lines using Nonintrusive Programmers' Biofeedback. ISSRE, pp. 93--103, 2019. |
| [S62] | T. Ishida & H. Uwano. Time series analysis of programmer's EEG for debug state classification. Programming, 2019, pp. 1--7. |
| [S63] | D. Roy, S. Fakhoury & V. Arnaoudova. VITALSE: Visualizing Eye Tracking and Biometric Data. ICSE, 2020. |
| ID | RQ1.Cognitive Load | RQ2.Sensors | RQ3.Metrics | RQ4.Machine Learning | RQ5.Purposes | RQ6.Tasks | RQ7.Artifacts | RQ8.Participants | RQ9.Research Methods | RQ10.Research Venue | Contributions |
|---|---|---|---|---|---|---|---|---|---|---|---|
| [S01] | Mental Effort | fMRI | Blood Oxygen Levels | Does not use | Code Comprehension | Programming | Source code | 17 | Validation Study | Journal | Empirical Knowledge |
| [S02] | Mental Effort | Eye-Tracking | Multimodal measurement | Does not use | Code Comprehension | Modelling | Business Process Model | 30 | Experience Paper | Conference | Lessons |
| [S03] | Cognitive Load | Multimodal sensors | Multimodal measurement | Multi-algorithms | Task Difficulty | Arithmetic Equation Calculation | Equation | 13 | Validation Study | Conference | Analysis |
| [S04] | Cognitive Load Theory | EEG | Event Related Desincronization (ERD) | Does not use | Level of Expertise | Programming | Source Code | 34 | Validation Study | Journal | Empirical Knowledge |
| [S05] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | 15 | Validation Study | Conference | Empirical Knowledge |
| [S06] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Naive Bayes | Task Difficulty | Programming | Source Code | 17 | Validation Study | Conference | Lessons |
| [S07] | Cognitive Performance | Multimodal Sensors | Multimodal Measurements | Multi Algorithms of classificaiton | Cognitive Load classification | Aritmetic Equation | Equation | 20 | Validation Study | Journal | Analysis |
| [S08] | Does not define | Multimodal Sensors | Multimodal Measurements | Decision Tree Classifier | Productivity | Programming | Source Code | 17 | Validation Study | Conference | Method |
| [S09] | Cognitive Performance | EEG | Individual Alpha Frequency (IAF) | Does not use | Code Comprehension | Programming | Source Code | 33 | Validation Study | Conference | Empirical Knowledge |
| [S10] | Does not define | Multimodal Sensors | Multimodal Measurements | Does not use | Emotion | Programming | Source Code | 30 | Replication Proposal | Workshop | Lessons |
| [S11] | Mental Effort | Eye-Tracking | Multimodal Measurements | Does not use | Code Comprehension | Modelling | Business Process Models | 75 | Validation Study | Journal | Empirical Knowledge |
| [S12] | Cognitive Performance | Multimodal Sensors | Event Related Desincronization (ERD) | Does not use | Emotion | Programming | Source Code | 50 | Phylosophical Paper | Journal | Lessons |
| [S13] | Cognitive Load Theory | Eye-Tracking | Multimodal Measurements | Does not use | Code Comprehension | Modelling | Business Process Models | 75 | Validation Study | Journal | Empirical Knowledge |
| [S14] | Cognitive Load Theory | Eye-Tracking | Multimodal Measurements | Does not use | Code Comprehension | Modelling | Business Process Models | 36 | Validation Study | Journal | Method |
| [S15] | Mental Effort | Multimodal Sensors | Does not specify | Does not use | Code Comprehension | Programming | Source Code | Does not identify | Proposal of Solution | Conference | Tools |
| [S16] | Cognitive Load | Does not use | Does not specify | Does not use | Productivity | Review | Source Code | 50 | Validation Study | Journal | Empirical Knowledge |
| [S17] | Does not define | EEG | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | 18 | Validation Study | Conference | Empirical Knowledge |
| [S18] | Does not define | Eye-Tracking | Eye Fixation | Does not use | Code Comprehension | Programming | Source Code | 22 | Validation Study | Conference | Method |
| [S19] | Does not define | Multimodal Sensors | Multimodal Measurements | Does not use | Work Interruption | Programming | Source Code | 10 | Validation Study | Conference | Method |
| [S20] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | 22 | Proposal of Solution | Workshop | Lessons |
| [S21] | Does not define | Eye-Tracking | Multimodal Measurements | Multi Algorithms of classificaiton | Stress | Programming | Source Code | 11 | Validation Study | Workshop | Method |
| [S22] | Mental Effort | fMRI | blood-oxygen levels | Does not use | Code Comprehension | Programming | Source Code | 11 | Replication Study | Conference | Empirical Knowledge |
| [S23] | Does not define | EEG | Multimodal Measurements | Support Vector Machine | Work Interruption | Review | Plain Text | 10 | Experience Paper | Conference | Lessons |
| [S24] | Cognitive Performance | EEG | Multimodal Measurements | Naive Bayes | Task Difficulty | Programming | Source Code | 20 | Validation Study | Conference | Method |
| [S25] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Multi Algorithms of classificaiton | Code Comprehension | Programming | Source Code | 28 | Replication Study | Conference | Method |
| [S26] | Does not define | fMRI | blood-oxygen levels | Does not use | Quality Concerns | Programming | Source Code | 17 | Validation Study | Conference | Empirical Knowledge |
| [S27] | Does not define | Eye-Tracking | Multimodal Measurements | Does not use | Task Difficulty | Programming | Source Code | 22 | Validation Study | Journal | Method |
| [S28] | Does not define | Eye-Tracking | Multimodal Measurements | Does not use | Identify code dependencies | Programming | Source Code | Does not identify | Proposal of Solution | Conference | Method |
| [S29] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Randon Forest Learners | Quality Concerns | Programming | Source Code | 10 | Validation Study | Conference | Analysis |
| [S30] | Does not define | Multimodal Sensors | Multimodal Measurements | Naive Bayes | Task Difficulty | Programming | Source Code | 17 | Phylosophical Paper | Conference | Lessons |
| [S31] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Does not use | Task Difficulty | Programming | Source Code | 22 | Validation Study | Workshop | Analysis |
| [S32] | Does not define | Multimodal Sensors | Multimodal Measurements | Naive Bayes | Work Interruption | Programming | Source Code | 33 | Validation Study | Conference | Method |
| [S33] | Cognitive Load Theory | Multimodal Sensors | Multimodal Measurements | Support Vector Machine | Level of Expertise | Programming | Source Code | 38 | Validation Study | Journal | Method |
| [S34] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Support Vector Machine | Cognitive Load classification | Programming | Source Code | 26 | Validation Study | Conference | Method |
| [S35] | Does not define | Multimodal Sensors | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | 5 | Validation Study | Workshop | Analysis |
| [S36] | Cognitive Load Theory | fNIR | blood-oxygen levels | Does not use | Programming learning | Programming | Source Code | 11 | Validation Study | Journal | Empirical Knowledge |
| [S37] | Does not define | Multimodal Sensors | Multimodal Measurements | Naive Bayes | Task Difficulty | Programming | Source Code | 15 | Validation Study | Conference | Method |
| [S38] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Multi Algorithms of classificaiton | Work Interruption | Memorization and solve arithmetic equation | Equation and Polygons | 20 | Validation Study | Conference | Tools |
| [S39] | Does not define | Multimodal Sensors | Multimodal Measurements | Multi Algorithms of classificaiton | Work Interruption | Programming | Source Code | 13 | Validation Study | Conference | Method |
| [S40] | Does not define | Multimodal Sensors | blood-oxygen levels | Does not use | Task Difficulty | Data Structures Rotation | Data structures | 76 | Validation Study | Conference | Empirical Knowledge |
| [S41] | Does not define | EEG | Frequency bands | Does not use | Task Difficulty | Programming | Source Code | 10 | Validation Study | Conference | Empirical Knowledge |
| [S42] | Does not define | Eye-Tracking | Multimodal Measurements | Does not use | Cognitive Load classification | Programming | Source Code | 11 | Validation Study | Conference | Method |
| [S43] | Does not define | fMRI | blood-oxygen levels | Does not use | Code Comprehension | Programming | Source Code | 17 | Validation Study | Conference | Empirical Knowledge |
| [S44] | Does not define | EEG | Event Related Desincronization (ERD) | Does not use | Code Comprehension | Programming | Source Code | 8 | Validation Study | Conference | Empirical Knowledge |
| [S45] | Does not define | fMRI | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code and Natural Language | 29 | Validation Study | Conference | Empirical Knowledge |
| [S46] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | 22 | Validation Study | Conference | Empirical Knowledge |
| [S47] | Mental Effort | Eye-Tracking | Eye Fixation | Does not use | Code Comprehension | Review | Plain Text | 15 | Replication Study | Conference | Empirical Knowledge |
| [S48] | Mental Workload | fNIR | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | Does not Specify | Validation Study | Conference | Empirical Knowledge |
| [S49] | Does not define | fMRI | blood-oxygen levels | Does not use | Quality Concerns | Programming | Source Code | 15 | Validation Study | Conference | Empirical Knowledge |
| [S50] | Mental Workload | fNIR | blood-oxygen levels | Does not use | Cognitive Load classification | Programming | Source Code | 10 | Validation Study | Conference | Empirical Knowledge |
| [S51] | Mental Workload | EEG | Frequency bands | Does not use | Productivity | Programming | Source Code | 6 | Validation Study | Workshop | Tools |
| [S52] | Mental Workload | EEG | Frequency bands | Does not use | Code Comprehension | Programming | Source Code | 17 | Validation Study | Conference | Empirical Knowledge |
| [S53] | Does not define | EMG | Frequency bands | Does not use | Task Difficulty | Programming | Source Code | 17 | Validation Study | Conference | Empirical Knowledge |
| [S54] | Does not define | fMRI | blood-oxygen levels | Does not use | Code Comprehension | Programming | Source Code | Does not Specify | Proposal of Solution | Conference | Method |
| [S55] | Cognitive Performance | EEG | Event Related Desincronization (ERD) | Does not use | Work Interruption | Programming | Source Code | 17 | Validation Study | Conference | Method |
| [S56] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code | 25 | Validation Study | Journal | Empirical Knowledge |
| [S57] | Does not define | fMRI | Multimodal Measurements | Does not use | Code Comprehension | Programming | Source Code and Natural Language | 30 | Validation Study | Conference | Empirical Knowledge |
| [S58] | Cognitive Performance | EEG | Multimodal Measurements | Multi Algorithms of classificaiton | Task Difficulty | Programming | Source Code | 9 | Validation Study | Conference | Method |
| [S59] | Mental Effort | Multimodal Sensors | Multimodal Measurements | Does not use | Cognitive Load classification | Programming | Source Code | 30 | Validation Study | Conference | Empirical Knowledge |
| [S60] | Mental Effort | EEG | Frequency bands | Does not use | Code Complexity | Programming | Source Code | 30 | Validation Study | Conference | Method |
| [S61] | Multimodal Sensors | Multimodal Measurements | Does not use | Quality Concerns | Programming | Source Code | 30 | Validation Study | Conference | Method | |
| [S62] | Does not define | EEG | Frequency bands | Does not use | Quality Concerns | Programming | Source Code | 5 | Validation Study | Conference | Analysis |
| [S63] | Mental Effort | Multimodal Sensors | Does not specify | Does not use | Code Comprehension | Programming | Source Code | Does not Specify | Proposal of Solution | Conference | Tools |