cs.SEMar 24, 2026
SaveMachine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
Organizations: Bernoulli Institute for Mathematics, Computer Science, and Artificial Intelligence, University of Groningen, Nijenborgh 9, 9747AG, Groningen, Groningen, The Netherlands
Abstract
Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques to allow for an early detection of burnout. This paper is a systematic literature review (SLR) of the research papers that proposed machine learning (ML) approaches, and focused on detecting burnout in software developers and IT professionals. Our objective is to review the accuracy and precision of the proposed ML techniques, and to formulate recommendations for future researchers interested to replicate or extend those studies. From our SLR we observed that a majority of primary studies focuses on detecting emotions or utilise emotional dimensions to detect or predict the presence of burnout. We also performed a cross-sectional study to detect which ML approach shows a better performance at detecting emotions; and which dataset has more potential and expressivity to capture emotions. We believe that, by identifying which ML tools and datasets show a better performance at detecting emotions, and indirectly at identifying burnout, our paper can be a valuable asset to progress in this important research direction.
Figures & tables
Figure 1: Pipeline used in this SLR to obtain the relevant primary studies.
| Inclusion Criteria | |
|---|---|
| IC1 | The paper must be a peer-reviewed paper and published in conferences, workshops, or journals. |
| IC2 | The paper must be accessible online (i.e., PDF files available in the selected databases or through Google). |
| IC3 | The study must be published between the years 2000 and 2025. |
| IC4 | The study discussed or mentioned the early detection of burnout, or reported machine learning as its approach to predict burnout in software engineering. |
| Exclusion Criteria | |
| EC1 | The non-English paper contains non-English data source used. |
Table 1: Inclusion and Exclusion Criteria
| No. | Questions | Focus |
|---|---|---|
| 1 | Are the aims clearly stated? | RQ1 |
| 2 | Does the paper clearly discuss the findings explaining the possibility of experiencing the risk of burnout or negative behaviour, or mention burnout or negative behaviour (e.g. negative emotion, feeling, stress or relationship)? | RQ1 |
| 3 | Does the paper utilised the machine learning approach(s)? | RQ1- RQ4 |
| 4 | Does the paper report the performance of the machine learning models? | RQ3 |
| 5 | Does the paper clearly state the datasets employed? | RQ4 |
Table 2: Assessment Criteria Form
| Categories | Paper ID | Machine Learning Techniques |
|---|---|---|
| C1 Emotional Dimension as Burnout Predictors | Mäntylä et al. (2016) | Linear Regression Mäntylä et al. (2016) , Zero R Mäntylä et al. (2016) |
| C2 Burnout Prediction | Nath et al. (2021) , Dovleac et al. (2021) | SVM Nath et al. (2021) , ANN Dovleac et al. (2021) |
| C3 Emotion. Dimension and Stress Detection | Girardi et al. (2020) ; Novielli et al. (2022) ; Nogueira et al. (2013) ; Girardi et al. (2021) ; Vrzakova et al. (2020) ; Fritz and Müller (2016) ; Nogueira et al. (2015) ; Naegelin et al. (2023) ; Epp et al. (2011) ; Srikanteswara et al. (2024) ; Dovleac et al. (2021) ; Gamage and Asanka (2022) ; Androutsou et al. (2023) ; Reddy et al. (2018) ; Booth et al. (2022) ; Padha and Sahoo (2022) ; Pepa et al. (2020) ; Alberdi et al. (2018) ; Vizer et al. (2009) ; Gachechiladze et al. (2017) ; Murgia et al. (2018) ; Imran (2024) ; Ballesteros et al. (2024) ; Bleyl and Buxton (2022) ; Maheshwarkar et al. (2021) ; Müller and Fritz (2015) ; Islam et al. (2019) ; Cabrera-Diego et al. (2020) ; Wagan and Li (2025) ; Yang et al. (2021) ; Singh et al. (2024) ; Soto et al. (2021) ; Anany et al. (2019) ; Klünder et al. (2020) ; Islam and Zibran (2018) ; Manikandan et al. (2024) ; Jayathilake et al. (2023) ; Carneiro et al. (2012) ; Rissler et al. (2020) , Manikandan et al. (2024) ; Geeth et al. (2024) ; Awan et al. (2023) ; Radevski et al. (2015) ; Kołakowska et al. (2013) ; Carneiro et al. (2012) | AB Androutsou et al. (2023) ; Rissler et al. (2020) , ANN Srikanteswara et al. (2024) , BERT Bleyl and Buxton (2022) ; Maheshwarkar et al. (2021) ; Singh et al. (2024) ; Imran (2024) NN Nogueira et al. (2015) ; Kołakowska et al. (2013) , DT Nogueira et al. (2013) ; Islam et al. (2019) ; Fritz and Müller (2016) ; Nogueira et al. (2015) ; Epp et al. (2011) ; Srikanteswara et al. (2024) ; Jayathilake et al. (2023) ; Gamage and Asanka (2022) ; Reddy et al. (2018) ; Rissler et al. (2020) ; Müller and Fritz (2015) ; Kołakowska et al. (2013) , GBT Islam et al. (2019) , J48 Girardi et al. (2020) ; Gachechiladze et al. (2017) , KNN Girardi et al. (2020) ; Girardi et al. (2021) ; Srikanteswara et al. (2024) ; Androutsou et al. (2023) ; Reddy et al. (2018) ; Padha and Sahoo (2022) ; Rissler et al. (2020) ; Geeth et al. (2024) ; Kołakowska et al. (2013) ; Carneiro et al. (2012) , MLP Girardi et al. (2020) ; Islam et al. (2019) ; Srikanteswara et al. (2024) ; Booth et al. (2022) , NB Girardi et al. (2020) ; Gachechiladze et al. (2017) ; Fritz and Müller (2016) ; Islam et al. (2019) ; Srikanteswara et al. (2024) ; Gamage and Asanka (2022) ; Rissler et al. (2020) ; Gachechiladze et al. (2017) ; Maheshwarkar et al. (2021) ; Kołakowska et al. (2013) , RF Girardi et al. (2020) ; Soto et al. (2021) ; Novielli et al. (2022) ; Vrzakova et al. (2020) ; Girardi et al. (2021) ; Nogueira et al. (2013) ; Nogueira et al. (2015) ; Naegelin et al. (2023) ; Srikanteswara et al. (2024) ; Gamage and Asanka (2022) ; Maheshwarkar et al. (2021) ; Androutsou et al. (2023) ; Reddy et al. (2018) ; Booth et al. (2022) ; Anany et al. (2019) ; Rissler et al. (2020) , SLP Nogueira et al. (2013) ; Rissler et al. (2020) , SVM Girardi et al. (2020) ; Novielli et al. (2022) ; Nogueira et al. (2013) ; Gachechiladze et al. (2017) ; Fritz and Müller (2016) ; Nogueira et al. (2015) ; Naegelin et al. (2023) ; Srikanteswara et al. (2024) ; Androutsou et al. (2023) ; Padha and Sahoo (2022) ; Rissler et al. (2020) ; Gachechiladze et al. (2017) ; Murgia et al. (2018) ; Geeth et al. (2024) ; Islam et al. (2019) ; Kołakowska et al. (2013) ; Manikandan et al. (2024) , GBM Naegelin et al. (2023) ; Androutsou et al. (2023) , Logistic Regression Srikanteswara et al. (2024) ; Gamage and Asanka (2022) ; Reddy et al. (2018) ; Yang et al. (2021) , Linear Regression Srikanteswara et al. (2024) , LSTM Geeth et al. (2024) ; Maheshwarkar et al. (2021) ; Yang et al. (2021) , CNN Manikandan et al. (2024) ; Geeth et al. (2024) ; Ballesteros et al. (2024) ; Maheshwarkar et al. (2021) ; Wagan and Li (2025) , RNN Dovleac et al. (2021) ; Geeth et al. (2024) ; Manikandan et al. (2024) , XGBoost Gamage and Asanka (2022) ; Androutsou et al. (2023) , CatBoost Gamage and Asanka (2022) , RUSBoost Androutsou et al. (2023) , ZeroR Rissler et al. (2020) , Voting Classifier(SVM, NB, and RF) Klünder et al. (2020) |
| C4 Sentiment and Emotions | Silva et al. (2023) , Pletea et al. (2014) ; Ortu et al. (2016) | Hierarchical Classifier Pletea et al. (2014) , NB Pletea et al. (2014) , RF Anany et al. (2019) , SentiStrength Silva et al. (2023) ; Ortu et al. (2016) |
| C5 Attrition prediction | Trinkenreich et al. (2024) ; Ozakca et al. (2024) ; Garcia et al. (2013) | AdaBoost Ozakca et al. (2024) , Bayesian Classfr. Garcia et al. (2013) DT Trinkenreich et al. (2024) ; Ozakca et al. (2024) , KNN Ozakca et al. (2024) , Logistic Regression Ozakca et al. (2024) , NB Ozakca et al. (2024) , RF Trinkenreich et al. (2024) ; Ozakca et al. (2024) , SVM Ozakca et al. (2024) |
| C6 Detecting Unhealthy Relationships as an Early Warning to Prevent Burnout | Raman et al. (2020) ; Cheriyan et al. (2021) ; Qiu et al. (2022) ; Sarker et al. (2023a) ; Ferreira et al. (2024) ; Sarker (2022) ; Mishra and Chatterjee (2024) ; Sarker et al. (2020) ; Bhat et al. (2021) ; Rahman et al. (2024) | CART Ferreira et al. (2024) , ChatGPT Mishra and Chatterjee (2024) ; Rahman et al. (2024) CNN Sarker et al. (2020) ; Sarker (2022) ; Raman et al. (2020) , DNN Sarker et al. (2023b) , DT Sarker et al. (2023b) ; Sarker (2022) , GBT Sarker et al. (2023b) ; Sarker (2022) , KNN Ferreira et al. (2024) , LR Sarker et al. (2023b) , Lexicon-based Sarker et al. (2023a) , LR Ferreira et al. (2024) ; Sarker (2022) , NB Ferreira et al. (2024) , RF Cheriyan et al. (2021) ; Sarker et al. (2023b) ; Ferreira et al. (2024) ; Sarker (2022) ; Qiu et al. (2022) , SVM Raman et al. (2020) ; Sarker et al. (2020) ; Cheriyan et al. (2021) ; Sarker et al. (2023b) ; Ferreira et al. (2024) ; Sarker (2022) , BERT Sarker et al. (2020) ; Cheriyan et al. (2021) ; Sarker et al. (2023b) ; Sarker et al. (2023a) ; Sinha et al. (2016) ; Sarker (2022) ; Raman et al. (2020) ; Bhat et al. (2021) ; Rahman et al. (2024) , LSTM Sarker et al. (2023b) ; Sarker (2022) |
Table 3: Table of analysed primary studies. Studies have employed more than one machine learning algorithm. Studies with bold fonts reported the performances in figures only or did not report the performances in their papers.
Figure 2: Number of papers grouped by purpose of study and input type.
| # of studies | studies | |
| Text-based | ||
| 3 | Silva et al. (2023) ; Pletea et al. (2014) ; Ortu et al. (2016) | |
| 24 | Mäntylä et al. (2016) ; Gachechiladze et al. (2017) ; Murgia et al. (2018) ; Islam and Zibran (2018) ; Imran (2024) ; Singh et al. (2024) ; Srikanteswara et al. (2024) ; Geeth et al. (2024) ; Gamage and Asanka (2022) ; Bleyl and Buxton (2022) ; Maheshwarkar et al. (2021) ; Islam et al. (2019) ; Reddy et al. (2018) ; Cabrera-Diego et al. (2020) ; Wagan and Li (2025) ; Muñoz and Iglesias (2022) ; Klünder et al. (2020) ; Nath et al. (2021) ; Vizer et al. (2009) , Girardi et al. (2020) ; Girardi et al. (2021) ; Müller and Fritz (2015) ; Androutsou et al. (2023) ; Yang et al. (2021) | |
| 11 | Raman et al. (2020) ; Cheriyan et al. (2021) ; Qiu et al. (2022) ; Sarker et al. (2023b) ; Sarker et al. (2023a) ; Ferreira et al. (2024) ; Sarker (2022) ; Mishra and Chatterjee (2024) ; Sarker et al. (2020) ; Bhat et al. (2021) ; Rahman et al. (2024) | |
| 3 | Trinkenreich et al. (2024) ; Ozakca et al. (2024) ; Garcia et al. (2013) | |
| Sensor-based |
Table 4: The list of studies grouped by their types of input and classification. All studies that employ sensors, movement, utterances, and facial expressions as their input are studies investigating emotions and stress detection. Bold studies use multiple input types, while regular studies use just one.
Figure 3: Model Performances of emotion and stress detection with machine learning techniques categorised based on their most generalised characteristics. Bayesian (e.g. Naïve Bayes), Lexicon based (e.g. Deva, Tensistrength, EmoTxt), Ensemble method (e.g. Adaptive Boast, XGBoost, CatBoost, Boosting), Instance-based algorithm (e.g. KNN), Linear Model (e.g. Logistic Regression), NN-based algorithms (e.g. MLP, SLP, Sentimoji, CNN), Ruled-based Algorithm (e.g. ZeroR), Tree-based Algorithms (e.g. DT, J48, RF), and Transformer-based Algorithms (e.g., GoEmotion, BERT, Albert, Roberta, Bert, Codebert, Graphcodebert)
Figure 4: Model Performances of Emotion and Stress Detection with sensor and movement data as the inputs and machine learning techniques categorised based on their most generalised characteristics. Bayesian (e.g. Naïve Bayes, Bayes Net), Ensemble method (e.g., AdaBoast, Light Gradient Boasting Method), Instance-based algorithm (e.g. KNN, K-star, IBk), Linear Model (e.g., Logistic Regression), NN-based algorithms (e.g. CNN, RestNet), Ruled-based Algorithm (e.g. ZeroR), Tree-based Algorithms (e.g., DT, J48, RF, C.45)
Figure 5: Model Performances of attrition detection with machine learning techniques categorised based on their most generalised characteristics. Bayesian (e.g. Naïve Bayes), Instance-based algorithm (e.g. KNN), Linear Model (e.g., Logistic Regression), NN-based algorithms (e.g. MLP), Tree-based Algorithms (e.g., DT, RF), Ensemble (e.g., Adaptive Boost), and Kernel-based algorithms (e.g., SVM, SVC)
Figure 6: Model Performances of toxicity detection with machine learning techniques categorised based on their most generalised characteristics. Bayesian (e.g. Naïve Bayes), Instance-based algorithm (e.g. KNN), Linear Model (e.g., Logistic Regression), NN-based algorithms (e.g. Deep Pyramid CNN, Strudel, DPCNN, DCRNN, LSTM, BiLSTM, and GRU), Tree-based Algorithms (e.g., DT, GBT, RF, CART), and Transformed-based model (e.g., BERT, RoBERTa, DistilBERT, ALBERT, XLNet, ChatGPT, and GPT4.0)
| Precision | Recall | F-score | Accuracy | |
|---|---|---|---|---|
| Emotion and Stress detection (using text) | Transformer-based model, Kernel-based | Transformer-based model, Kernel-based model | Kernel-based model | Bayesian |
| Emotion and Stress detection (using sensors and movement) | Bayesian | Bayesian | Instance-based model | Ensemble |
| Attrition Prediction | Tree-based model | Tree-based model | Tree-based, Ensemble | Tree-based |
| Toxicity detection | NN-based model, Transformer-based model | Transformer-based model | NN-based model | NN-based model, Tree-based model |
Table 5: Summary of performance measurements - RQ3
Figure 7: Model Performances by datasets
| Precision | Recall | F-score | Accuracy | |
|---|---|---|---|---|
| Bugzilla | M | M | M | |
| Combination | M | |||
| Code Review (CR) | H | H | H | H |
| Gerrit | H | h | h | h |
| Github | h | h | M | L |
| Gitter | H | H | H | H |
Table 6: Summary of performance measurements - RQ4. H, h, M, and L represent the performance value (median) with the criteria respectively: more than 0.8; between 0.7 and 0.8; between 0.5 and 0.7; below 0.5
Figure 8: Trends of classification during the period
Figure 9: A framework of machine-learning based early detection of burnout
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