Liming Fu

Liming Fu, Peng Liang, Xueying Li et al.

Stakeholders make various types of decisions with respect to requirements, design, management, and so on during the software development life cycle. Nevertheless, these decisions are typically not well documented and classified due to limited human resources, time, and budget. To this end, automatic approaches provide a promising way. In this paper, we aimed at automatically classifying decisions into five types to help stakeholders better document and understand decisions. First, we collected a dataset from the Hibernate developer mailing list. We then experimented and evaluated 270 configurations regarding feature selection, feature extraction techniques, and machine learning classifiers to seek the best configuration for classifying decisions. Especially, we applied an ensemble learning method and constructed ensemble classifiers to compare the performance between ensemble classifiers and base classifiers. Our experiment results show that (1) feature selection can decently improve the classification results; (2) ensemble classifiers can outperform base classifiers provided that ensemble classifiers are well constructed; (3) BoW + 50% features selected by feature selection with an ensemble classifier that combines Naïve Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) achieves the best classification result (with a weighted precision of 0.750, a weighted recall of 0.739, and a weighted F1-score of 0.727) among all the configurations. Our work can benefit various types of stakeholders in software development through providing an automatic approach for effectively classifying decisions into specific types that are relevant to their interests.

Chen Yang, Peng Liang, Liming Fu et al.

Deep learning (DL) frameworks have been extensively designed, implemented, and used in software projects across many domains. However, due to the lack of knowledge or information, time pressure, complex context, etc., various uncertainties emerge during the development, leading to assumptions made in DL frameworks. Though not all the assumptions are negative to the frameworks, being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures, inconsistencies, and increased cost). As the first step of addressing the critical problems, there is a need to explore and understand the assumptions made in DL frameworks. To this end, we conducted an exploratory study to understand self-claimed assumptions (SCAs) about their distribution, classification, and impacts using code comments from nine popular DL framework projects on GitHub. The results are that: (1) 3,084 SCAs are scattered across 1,775 files in the nine DL frameworks, ranging from 1,460 (TensorFlow) to 8 (Keras) SCAs. (2) There are four types of validity of SCAs: Valid SCA, Invalid SCA, Conditional SCA, and Unknown SCA, and four types of SCAs based on their content: Configuration and Context SCA, Design SCA, Tensor and Variable SCA, and Miscellaneous SCA. (3) Both valid and invalid SCAs may have an impact within a specific scope (e.g., in a function) on the DL frameworks. Certain technical debt is induced when making SCAs. There are source code written and decisions made based on SCAs. This is the first study on investigating SCAs in DL frameworks, which helps researchers and practitioners to get a comprehensive understanding on the assumptions made. We also provide the first dataset of SCAs for further research and practice in this area.