Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally rest on the relationships between metric values and defect-proneness of classes. The objective of this study is to empirically examine whether there are effective threshold values of OO metrics by analyzing existing threshold derivation methods with a large-scale meta-analysis. Based on five representative threshold derivation methods (i.e. VARL, ROC, BPP, MFM, and MGM) and 3268 releases from 65 Java projects, we first employ statistical meta-analysis and sensitivity analysis techniques to derive thresholds for 62 OO metrics on the training data. Then, we investigate the predictive performance of five candidate thresholds for each metric on the validation data to explore which of these candidate thresholds can be served as the threshold. Finally, we evaluate their predictive performance on the test data. The experimental results show that 26 of 62 metrics have the threshold effect and the derived thresholds by meta-analysis achieve promising results of GM values and significantly outperform almost all five representative (baseline) thresholds.

Background. Code-line-level bugginess identification (CLBI) is a vital technique that can facilitate developers to identify buggy lines without expending a large amount of human effort. Most of the existing studies tried to mine the characteristics of source codes to train supervised prediction models, which have been reported to be able to discriminate buggy code lines amongst others in a target program. Problem. However, several simple and clear code characteristics, such as complexity of code lines, have been disregarded in the current literature. Such characteristics can be acquired and applied easily in an unsupervised way to conduct more accurate CLBI, which also can decrease the application cost of existing CLBI approaches by a large margin. Objective. We aim at investigating the status quo in the field of CLBI from the perspective of (1) how far we have really come in the literature, and (2) how far we have yet to go in the industry, by analyzing the performance of state-of-the-art (SOTA) CLBI approaches and tools, respectively. Method. We propose a simple heuristic baseline solution GLANCE (aimin G at contro L - AN d C ompl E x-statements) with three implementations (i.e., GLANCE-MD, GLANCE-EA, and GLANCE-LR). GLANCE is a two-stage CLBI framework: first, use a simple model to predict the potentially defective files; second, leverage simple code characteristics to identify buggy code lines in the predicted defective files. We use GLANCE as the baseline to investigate the effectiveness of the SOTA CLBI approaches, including natural language processing (NLP) based, model interpretation techniques (MIT) based, and popular static analysis tools (SAT). Result. Based on 19 open-source projects with 142 different releases, the experimental results show that GLANCE framework has a prediction performance comparable or even superior to the existing SOTA CLBI approaches and tools in terms of 8 different performance indicators. Conclusion. The results caution us that, if the identification performance is the goal, the real progress in CLBI is not being achieved as it might have been envisaged in the literature and there is still a long way to go to really promote the effectiveness of static analysis tools in industry. In addition, we suggest using GLANCE as a baseline in future studies to demonstrate the usefulness of any newly proposed CLBI approach.

Static analysis (SA) tools can generate useful static warnings to reveal the problematic code snippets in a software system without dynamically executing the corresponding source code. In the literature, static warnings are of paramount importance because they can easily indicate specific types of software defects in the early stage of a software development process, which accordingly reduces the maintenance costs by a substantial margin. Unfortunately, due to the conservative approximations of such SA tools, a large number of false positive (FP for short) warnings (i.e., they do not indicate real bugs) are generated, making these tools less effective. During the past two decades, therefore, many false positive mitigation (FPM for short) approaches have been proposed so that more accurate and critical warnings can be delivered to developers. This paper offers a detailed survey of research achievements on the topic of FPM. Given the collected 130 surveyed papers, we conduct a comprehensive investigation from five different perspectives. First, we reveal the research trends of this field. Second, we classify the existing FPM approaches into five different types and then present the concrete research progress. Third, we analyze the evaluation system applied to examine the performance of the proposed approaches in terms of studied SA tools, evaluation scenarios, performance indicators, and collected datasets, respectively. Fourth, we summarize the four types of empirical studies relating to SA warnings to exploit the insightful findings that are helpful to reduce FP warnings. Finally, we sum up 10 challenges unresolved in the literature from the aspects of systematicness, effectiveness, completeness, and practicability and outline possible research opportunities based on three emerging techniques in the future.

The label quality of defect data sets has a direct influence on the reliability of defect prediction models. In this paper, we conduct a systematic study of inconsistent defect labels in multi-version-project defect data sets, i.e., many instances having the same source code but different labels over multiple versions of a software project. First, we report the phenomena of inconsistent labels by real examples and analyze their essence in the context of defect prediction. Then, we uncover the causes that lead to the occurrence of inconsistent labels for the representative label collection approaches. Finally, we investigate the actual influence of inconsistent labels on defect prediction models. We find that inconsistent labels in general exist in six multi-version-project defect data sets (either widely used or the most up-to-date in the literature) collected by diverse label collection approaches. In particular, inconsistent labels in a training data set significantly reduce the prediction performance of a model, while inconsistent labels in a test data set can lead to a considerable evaluation bias on the real performance. Therefore, we recommend that: on the one hand, researchers leverage our findings to make targeted methodological improvements on existing defect label collection approaches to reduce the generation of inconsistent labels; on the other hand, practitioners detect and exclude inconsistent labels in defect data sets to avoid their potential negative influence on defect prediction.

Mixed reality offers a larger visualization space and more intuitive means of interaction for data exploration, and many works have dedicated on combining 2D visualizations on screen with mixed reality. However, for each combination, we need to customize the implementation of the corresponding mixed reality 3D visualization. It is a challenge to simplify this development process and enable agile building of mixed reality 3D visualizations for 2D visualizations. In addition, many existing 2D visualizations do not provide interfaces oriented to immersive analytic, so how to extend the mixed reality 3D space from existing 2D visualizations is another challenge. This work presents an agile and flexible approach to interactively transfer visualizations from 2D screens to mixed reality 3D spaces. We designed an interactive process for spatial generation of mixed-reality 3D visualizations, defined a unified data transfer framework, integrated data deconstruction techniques for 2D visualizations, implemented interfaces to immersive visualization building tool-kits, and encapsulated these techniques into a tool named X-Space. We validated that the approach is feasible and effective through 2D visualization cases including scatter plots, stacked bar charts, and adjacency matrix. Finally we conducted expert interviews to discuss the usability and value of the method.

Background. Self-admitted technical debt (SATD) is a special kind of technical debt that is intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many automatic approaches have been proposed to identify SATD. Problem. Popular IDEs support a number of predefined task annotation tags for indicating SATD in comments, which have been used in many projects. However, such clear prior knowledge is neglected by existing SATD identification approaches when identifying SATD. Objective. We aim to investigate how far we have really progressed in the field of SATD identification by comparing existing approaches with a simple approach that leverages the predefined task tags to identify SATD. Method. We first propose a simple heuristic approach that fuzzily Matches task Annotation Tags ( MAT ) in comments to identify SATD. In nature, MAT is an unsupervised approach, which does not need any data to train a prediction model and has a good understandability. Then, we examine the real progress in SATD identification by comparing MAT against existing approaches. Result. The experimental results reveal that: (1) MAT has a similar or even superior performance for SATD identification compared with existing approaches, regardless of whether non-effort-aware or effort-aware evaluation indicators are considered; (2) the SATDs (or non-SATDs) correctly identified by existing approaches are highly overlapped with those identified by MAT ; and (3) supervised approaches misclassify many SATDs marked with task tags as non-SATDs, which can be easily corrected by their combinations with MAT . Conclusion. It appears that the problem of SATD identification has been (unintentionally) complicated by our community, i.e., the real progress in SATD comments identification is not being achieved as it might have been envisaged. We hence suggest that, when many task tags are used in the comments of a target project, future SATD identification studies should use MAT as an easy-to-implement baseline to demonstrate the usefulness of any newly proposed approach.