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Software defect prediction is a subject of study involving the interplay of software engineering and machine learning. The current literature proposed numerous machine learning models to predict software defects from software data, such as ...

The study examines the developers' perceptions of quality attributes for defect prediction. The survey found that code complexity was relevant to avoid defects, whereas models prioritize documentation. The thematic analysis revealed that testing is ...

Line‐level software defect prediction (LL‐SDP) serves as a valuable tool for developers to detect defective lines with minimal human effort. Recently, GLANCE was proposed as a readily implementable baseline for assessing the efficacy ...

We conducted a thorough comparative analysis between DeepLineDP, a deep learning‐based line‐level defect prediction (LL‐SDP) approach, and GLANCE, a recently introduced, simpler, traditional LL‐SDP approach. Our findings reveal that the application of ...

Code changes can introduce defects that affect software quality and reliability. Just-in-time (JIT) defect prediction techniques provide feedback at check-in time on whether a code change is likely to contain defects. This immediate feedback allows ...

Software quality prediction models play a crucial role in identifying vulnerable software components during early stages of development, and thereby optimizing the resource allocation and enhancing the overall software quality. While ...

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 ...

In this paper, we present ApacheJIT, a large dataset for Just-In-Time (JIT) defect prediction. ApacheJIT consists of clean and bug-inducing software changes in 14 popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and ...

Cross‐project defect prediction (CPDP), where data from different software projects are used to predict defects, has been proposed as a way to provide data for software projects that lack historical data. Evaluations of CPDP models using the ...

What you change is what you fuzz! In an empirical study of all fuzzer-generated bug reports in OSSFuzz, we found that four in every five bugs have been introduced by recent code changes. That is, 77% of 23k bugs are regressions. For a newly added ...

Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true.

To demonstrate this, we analyzed hundreds of popular GitHub projects. These projects ran ...

We report here the reuse of oversampling, and modifications to the basic approach, used in a recent TSE ’21 paper by YedidaMenzies. The method reused is the oversampling technique studied by Buda et al. These methods were studied in the SE domain (...

Software project defect prediction can help developers allocate debugging resources. Existing software defect prediction models are usually based on machine learning methods, especially deep learning. Deep learning‐based methods tend to build ...

Source code‐based automatic representations are more objective and accurate than traditional handcrafted metrics. This article proposed a new framework to represent code called multiperspective tree embedding (MPT‐embedding), which is an unsupervised ...

Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited ...

Software defects can lead to software failures or errors at any time. Therefore, software developers and engineers spend a lot of time and effort in order to find possible defects. This paper proposes an automatic approach to predict software defects ...

Software defect prediction heavily relies on the metrics collected from software projects. Earlier studies often used machine learning techniques to build, validate, and improve bug prediction models using either a set of metrics collected within a ...

Software defect prediction technology is an effective method to improve software quality. Effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify more defective changes in limited effort. Although many methods have been proposed ...

With the rise of AI-based systems, such as self-driving cars, Google search, and automated decision-making systems, new challenges have emerged for the testing community. Verifying such software systems is becoming an extremely difficult and expensive ...

In order to develop and train defect prediction models, researchers rely on datasets in which a defect is often attributed to a release where the defect itself is discovered. However, in many circumstances, it can happen that a defect is only discovered ...

Defects are common in software systems and cause many problems for software users. Different methods have been developed to make early prediction about the most likely defective modules in large codebases. Most focus on designing features (e.g. ...

According to psychological scientists, humans understand models that most match their own internal models, which they characterize as lists of "heuristic"s (i.e. lists of very succinct rules). One such heuristic rule generator is the Fast-and-Frugal ...

Network measures have been proved to be useful in predicting software defects. Leveraging the dependency relationships between software modules, network measures can capture various structural features of software systems. However, existing studies have ...