#Robotstudio background task software#

In: Proceedings of the 34th International Conference on Software Engineering, ICSE РІР‚в„ў12, pp. Han, S., Dang, Y., Ge, S., Zhang, D., Xie, T.: Performance debugging in the large via mining millions of stack traces. In: Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications, OOPSLA РІР‚в„ў12, pp. Gu, Z., Barr, E.T., Schleck, D., Su, Z.: Reusing debugging knowledge via trace-based bug search. In: Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, pp. Glerum, K., Kinshumann, K., Greenberg, S., Aul, G., Orgovan, V., Nichols, G., Grant, D., Loihle, G., Hunt, G.: Debugging in the (very) large: ten years of implementation and experience. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, vol. In: 2011 27th IEEE International Conference on Software Maintenance (ICSM), pp. Р”Вhaliwal, T., Khomh, F., Zou, Y.: Classifying field crash reports for fixing bugs: a case study of mozilla firefox. In: 2012 19th Working Conference on Reverse Engineering, pp. Р”Вavies, S., Roper, M., Wood, M.: Using bug report similarity to enhance bug localisation. Р”Вang, Y., Wu, R., Zhang, H., Zhang, D., Nobel, P.: Rebucket: a method for clustering duplicate crash reports based on call stack similarity. Р”Вamevski, K., Chen, H., Shepherd, D.C., Kraft, N.A., Pollock, L.: Predicting future developer behavior in the IDE using topic models. Р”Вamevski, K., Shepherd, D.C., Schneider, J., Pollock, L.: Mining sequences of developer interactions in visual studio for usage smells. In: Proceedings of the 13th International Conference on Mining Software Repositories, MSR РІР‚в„ў16, pp. Р”Вamevski, K., Chen, H., Shepherd, D., Pollock, L.: Interactive exploration of developer interaction traces using a hidden Markov model. Р”В¬hou, A., Yang, J., Chelf, B., Hallem, S., Engler, D.: An empirical study of operating systems errors. Р”В¬hen, T.H., Thomas, S.W., Hassan, A.E.: A survey on the use of topic models when mining software repositories. In: 2007 IEEE 11th International Conference on Computer Vision, pp. Р”В¬ao, L., Fei-Fei, L.: Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes. 3(Jan), 993РІР‚вЂњ1022 (2003)Р”В«lei, D.M., Griffiths, T.L., Jordan, M.I.: The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Р”В«lei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR РІР‚в„ў01, pp. IEEE (2007)Р”В«lei, D.M., Moreno, P.J.: Topic segmentation with an aspect hidden Markov model. In: 2007 IEEE International Parallel and Distributed Processing Symposium, p.Р’В 64. 98, 74РІР‚вЂњ88 (2018)Р”Р„rnold, D.C., Ahn, D.H., DeР’В Supinski, B.R., Lee, G.L., Miller, B.P., Schulz, M.: Stack trace analysis for large scale debugging.

#Robotstudio background task how to#

We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.Р”Р„grawal, A., Fu, W., Menzies, T.: What is wrong with topic modeling? And how to fix it using search-based software engineering. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions.

This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. Therefore, we propose a probabilistic unsupervised learning approach, adapting the nested hierarchical Dirichlet process, which is a Bayesian non-parametric hierarchical topic model originally applied to natural language data. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. The model described in this paper aims to improve developersРІР‚в„ў comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production-software fault reports consisting of software exceptions and stack traces. Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing.