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Software systems are often developed and released without formal specifications. For those systems that are formally specified, developers have to continuously maintain and update the specifications or have them fall out of date. To deal with the absence of formal specifications, researchers have proposed techniques to infer the missing specifications of an implementation in a variety of forms, such as finite state automaton (FSA). Despite the progress in this area, the efficacy of the proposed specification miners needs to improve if these miners are to be adopted.We propose SpecForge, a new specification mining approach that synergizes many existing specification miners. SpecForge decomposes FSAs that are inferred by existing miners into simple constraints, through a process we refer to as model fission. It then filters the outlier constraints and fuses the constraints back together into a single FSA (i.e., model fusion). We have evaluated SpecForge on execution traces of 10 programs, which includes 5 programs from DaCapo benchmark, to infer behavioral models of 13 library classes. Our results show that SpecForge achieves an average precision, recall and F-measure of 90.57%, 54.58%, and 64.21% respectively. SpecForge outperforms the best performing baseline by 13.75% in terms of F-measure.

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Abstract-Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of examples is both time-consuming and labor-intensive. Thus, reducing the onerous burden of manual labelling while still being able to achieve good performance is crucial towards the adoption of such approaches. To deal with this challenge, in this work, we propose an active semi-supervised defect prediction approach. It is performed by actively selecting a small subset of diverse and informative defect examples to label (i.e., active learning), and by making use of both labeled and unlabeled defect examples in the prediction model learning process (i.e., semi-supervised learning). Using this principle, our approach is able to learn a good model while minimizing the manual labeling effort.To evaluate the effectiveness of our approach, we make use of a benchmark dataset that contains 500 defects from three software systems that have been manually labelled into several families based on ODC. We investigate our approach's ability in achieving good classification performance, measured in terms of weighted precision, recall, F-measure, and AUC, when only a small number of manually labelled defect examples are available. Our experiment results show that our active semi-supervised defect categorization approach is able to achieve a weighted precision, recall, F-measure, and AUC of 0.651, 0.669, 0.623, and 0.710, respectively, when only 50 defects are manually labelled. Furthermore, it outperforms an existing active multiclass classification algorithm, proposed in the machine learning community, by a substantial margin.