Empirical Software Engineering最新文献

Just-in-Time software defect prediction (JIT-SDP) prevents the introduction of defects into the software by identifying them at commit check-in time. Current software defect prediction approaches rely on manually crafted features such as change metrics and involve expensive to train machine learning or deep learning models. These models typically involve extensive training processes that may require significant computational resources and time. These characteristics can pose challenges when attempting to update the models in real-time as new examples become available, potentially impacting their suitability for fast online defect prediction. Furthermore, the reliance on a complex underlying model makes these approaches often less explainable, which means the developers cannot understand the reasons behind models’ predictions. An approach that is not explainable might not be adopted in real-life development environments because of developers’ lack of trust in its results. To address these limitations, we propose an approach called IRJIT that employs information retrieval on source code and labels new commits as buggy or clean based on their similarity to past buggy or clean commits. IRJIT approach is online and explainable as it can learn from new data without expensive retraining, and developers can see the documents that support a prediction, providing additional context. By evaluating 10 open-source datasets in a within project setting, we show that our approach is up to 112 times faster than the state-of-the-art ML and DL approaches, offers explainability at the commit and line level, and has comparable performance to the state-of-the-art.

Despite the accuracy of machine learning (ML) techniques in predicting invalid bug reports, as shown in earlier research, and the importance of early identification of invalid bug reports in software maintenance, the adoption of ML techniques for this task in industrial practice is yet to be investigated. In this study, we used a technology transfer model to guide the adoption of an ML technique at a company for the early identification of invalid bug reports. In the process, we also identify necessary conditions for adopting such techniques in practice. We followed a case study research approach with various design and analysis iterations for technology transfer activities. We collected data from bug repositories, through focus groups, a questionnaire, and a presentation and feedback session with an expert. As expected, we found that an ML technique can identify invalid bug reports with acceptable accuracy at an early stage. However, the technique’s accuracy drops over time in its operational use due to changes in the product, the used technologies, or the development organization. Such changes may require retraining the ML model. During validation, practitioners highlighted the need to understand the ML technique’s predictions to trust the predictions. We found that a visual (using a state-of-the-art ML interpretation framework) and descriptive explanation of the prediction increases the trustability of the technique compared to just presenting the results of the validity predictions. We conclude that trustability, integration with the existing toolchain, and maintaining the techniques’ accuracy over time are critical for increasing the likelihood of adoption.

Due to the prevalent application of machine learning (ML) techniques and the intrinsic black-box nature of ML models, the need for good explanations that are sufficient and necessary towards locally interpreting a model’s prediction has been well recognized and emphasized. Existing explanation approaches, however, favor either the sufficiency or necessity. To fill this gap, in this paper, we propose an approach for generating local explanations that are both sufficient and necessary. Our approach, DDImage, automatically produces local explanations for ML-based image classifiers in a post-hoc way. The core idea behind DDImage is to discover an appropriate explanation by debugging the given input image via a series of image reductions, with respect to the sufficiency and necessity properties. Evaluation of DDImage using publicly available datasets and popular classification models reveals its effectiveness and efficiency. Compared with three state-of-the-art approaches, DDImage demonstrates a superior performance in producing small-sized explanations preserving both sufficiency and necessity, and it also shows promising stability and efficiency. We also identify the impact of segmentation granularity, reveal the performance variance for different target models, and further show that our approach is applicable across different problem domains.

[Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR’s first bug assignment without human intervention happened in April 2019. [Objective] Our study evaluates the adoption of TRR within its industrial context at Ericsson, i.e., we provide lessons learned related to the productization of a research prototype within a company. Moreover, we investigate 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. [Method] We conduct a preregistered industrial case study combining interviews with TRR stakeholders, minutes from sprint planning meetings, and bug-tracking data. The data analysis includes thematic analysis, descriptive statistics, and Bayesian causal analysis. [Results] TRR is now an incorporated part of the bug assignment process. Considering the abstraction levels of the telecommunications stack, high-level modules are more positive while low-level modules experienced some drawbacks. Most importantly, some bug reports directly reach low-level modules without first having passed through fundamental root-cause analysis steps at higher levels. On average, TRR automatically assigns 30% of the incoming bug reports with an accuracy of 75%. Auto-routed TRs are resolved around 21% faster within Ericsson, and TRR has saved highly seasoned engineers many hours of work. Indirect effects of adopting TRR include process improvements, process awareness, increased communication, and higher job satisfaction. [Conclusions] TRR has saved time at Ericsson, but the adoption of automated bug assignment was more intricate compared to similar endeavors reported from other companies. We primarily attribute the difference to the very large size of the organization and the complex products. Key facilitators in the successful adoption include a gradual introduction, product champions, and careful stakeholder analysis.

Building past snapshots of a software project has shown to be of interest both for researchers and practitioners. However, little attention has been devoted specifically to tests available in those past snapshots, which are fundamental for the maintenance of old versions still in production. The aim of this study is to determine to which extent tests of past snapshots can be executed successfully, which would mean these past snapshots are still testable. Given a software project, we build all its past snapshots from source code, including tests, and then run the tests. When tests do not result in success, we also record the reasons, allowing us to determine factors that make tests fail. We run this method on a total of 86 Java projects. On average, for 52.53% of the project snapshots on which tests can be built, all tests pass. However, on average, 94.14% of tests pass in previous snapshots when we take into account the percentage of tests passing in the snapshots used for building those tests. In real software projects, successfully running tests in past snapshots is not something that we can take for granted: we have found that in a large proportion of the projects we studied this does not happen frequently. We have found that the building from source code is the main limitation when running tests on past snapshots. However, we have found some projects for which tests run successfully in a very large fraction of past snapshots, which allows us to identify good practices. We also provide a framework and metrics to quantify testability (the extent to which we are able to run tests of a snapshot with a success result) of past snapshots from several points of view, which simplifies new analyses on this matter, and could help to measure how any project performs in this respect.

The growing software development market has increased the demand for qualified professionals in Software Engineering (SE). To this end, companies must enhance their Recruitment and Selection (R&S) processes to maintain high-quality teams, including opening opportunities for beginners, such as trainees and interns. However, given the various judgments and sociotechnical factors involved, this complex process of R&S poses a challenge for recent graduates seeking to enter the market. This paper aims to identify a set of anti-patterns and recommendations for early career SE professionals concerning R&S processes. Under an exploratory and qualitative methodological approach, we conducted six online Focus Groups with 18 recruiters with experience in R&S in the software industry. After completing our qualitative analysis, we identified 12 anti-patterns and 31 actionable recommendations regarding the hiring process focused on entry-level SE professionals. The identified anti-patterns encompass behavioral and technical dimensions innate to R&S processes. These findings provide a rich opportunity for reflection in the SE industry and offer valuable guidance for early-career candidates and organizations. From an academic perspective, this work also raises awareness of the intersection of Human Resources and SE, an area with considerable potential to be expanded in the context of cooperative and human aspects of SE.

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys ’s implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams.

Deep Neural Network (DNN) models are widely used in many cutting-edge domains, such as medical diagnostics and autonomous driving. However, an urgent need to test DNN models thoroughly has increasingly risen. Recent research proposes various structural and non-structural coverage criteria to measure test adequacy. Structural coverage criteria quantify the degree to which the internal elements of DNN models are covered by a test suite. However, they convey little information about individual inputs and exhibit limited correlation with defect detection. Additionally, existing non-structural coverage criteria are unaware of neurons’ importance to decision-making. This paper addresses these limitations by proposing novel non-structural coverage criteria. By tracing neurons’ cumulative contribution to the final decision on the training set, this paper identifies important neurons of DNN models. A novel metric is proposed to quantify the difference in important neuron behavior between a test input and the training set, which provides a measured way at individual test input granularity. Additionally, two non-structural coverage criteria are introduced that allow for the quantification of test adequacy by examining differences in important neuron behavior between the testing and the training set. The empirical evaluation of image datasets demonstrates that the proposed metric outperforms the existing non-structural adequacy metrics by up to 14.7% accuracy improvement in capturing error-revealing test inputs. Compared with state-of-the-art coverage criteria, the proposed coverage criteria are more sensitive to errors, including natural errors and adversarial examples.

Context

Representing the textual semantics of bug reports is a key component of bug report management (BRM) techniques. Existing studies mainly use classical information retrieval-based (IR-based) approaches, such as the vector space model (VSM) to do semantic extraction. Little attention is paid to exploring whether word embedding (WE) models from the natural language process could help BRM tasks.

Objective

To have a general view of the potential of word embedding models in representing the semantics of bug reports and attempt to provide some actionable guidelines in using semantic retrieval models for BRM tasks.

Method

We studied the efficacy of five widely recognized WE models for six BRM tasks on 20 widely-used products from the Eclipse and Mozilla foundations. Specifically, we first explored the suitable machine learning techniques under the use of WE models and the suitable WE model for BRM tasks. Then we studied whether WE models performed better than classical VSM. Last, we investigated whether WE models fine-tuned with bug reports outperformed general pre-trained WE models.

Key Results

The Random Forest (RF) classifier outperformed other typical classifiers under the use of different WE models in semantic extraction.We rarely observed statistically significant performance differences among five WE models in five BRM classification tasks, but we found that small-dimensional WE models performed better than larger ones in the duplicate bug report detection task. Among three BRM tasks (i.e., bug severity prediction, reopened bug prediction, and duplicate bug report detection) that showed statistically significant performance differences, VSM outperformed the studied WE models. We did not find performance improvement after we fine-tuned general pre-trained BERT with bug report data.

Conclusion

Performance improvements of using pre-trained WE models were not observed in studied BRM tasks. The combination of RF and traditional VSM was found to achieve the best performance in various BRM tasks.