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Register a new userA Simulation Study of Bandit Algorithms to Address External Validity of Software Fault Prediction
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Added by
Masateru Tsunoda
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Various software fault prediction models and techniques for building algorithms have been proposed. Many studies have compared and evaluated them to identify the most effective ones. However, in most cases, such models and techniques do not have the best performance on every dataset. This is because there is diversity of software development datasets, and therefore, there is a risk that the selected model or technique shows bad performance on a certain dataset. To avoid selecting a low accuracy model, we apply bandit algorithms to predict faults. Consider a case where player has 100 coins to bet on several slot machines. Ordinary usage of software fault prediction is analogous to the player betting all 100 coins in one slot machine. In contrast, bandit algorithms bet one coin on each machine (i.e., use prediction models) step-by-step to seek the best machine. In the experiment, we developed an artificial dataset that includes 100 modules, 15 of which include faults. Then, we developed various artificial fault prediction models and selected them dynamically using bandit algorithms. The Thomson sampling algorithm showed the best or second-best prediction performance compared with using only one prediction model.
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Background. Developers spend more time fixing bugs and refactoring the code to increase the maintainability than developing new features. Researchers investigated the code quality impact on fault-proneness focusing on code smells and code metrics. Objective. We aim at advancing fault-inducing commit prediction based on SonarQube considering the contribution provided by each rule and metric. Method. We designed and conducted a case study among 33 Java projects analyzed with SonarQube and SZZ to identify fault-inducing and fault-fixing commits. Moreover, we investigated fault-proneness of each SonarQube rule and metric using Machine and Deep Learning models. Results. We analyzed 77,932 commits that contain 40,890 faults and infected by more than 174 SonarQube rules violated 1,9M times, on which there was calculated 24 software metrics available by the tool. Compared to machine learning models, deep learning provide a more accurate fault detection accuracy and allowed us to accurately identify the fault-prediction power of each SonarQube rule. As a result, fourteen of the 174 violated rules has an importance higher than 1% and account for 30% of the total fault-proneness importance, while the fault proneness of the remaining 165 rules is negligible. Conclusion. Future works might consider the adoption of timeseries analysis and anomaly detection techniques to better and more accurately detect the rules that impact fault-proneness.
Background: Unsupervised machine learners have been increasingly applied to software defect prediction. It is an approach that may be valuable for software practitioners because it reduces the need for labeled training data. Objective: Investigate the use and performance of unsupervised learning techniques in software defect prediction. Method: We conducted a systematic literature review that identified 49 studies containing 2456 individual experimental results, which satisfied our inclusion criteria published between January 2000 and March 2018. In order to compare prediction performance across these studies in a consistent way, we (re-)computed the confusion matrices and employed the Matthews Correlation Coefficient (MCC) as our main performance measure. Results: Our meta-analysis shows that unsupervised models are comparable with supervised models for both within-project and cross-project prediction. Among the 14 families of unsupervised model, Fuzzy CMeans (FCM) and Fuzzy SOMs (FSOMs) perform best. In addition, where we were able to check, we found that almost 11% (262/2456) of published results (contained in 16 papers) were internally inconsistent and a further 33% (823/2456) provided insufficient details for us to check. Conclusion: Although many factors impact the performance of a classifier, e.g., dataset characteristics, broadly speaking, unsupervised classifiers do not seem to perform worse than the supervised classifiers in our review. However, we note a worrying prevalence of (i) demonstrably erroneous experimental results, (ii) undemanding benchmarks and (iii) incomplete reporting. We therefore encourage researchers to be comprehensive in their reporting.
This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.
Background: Studies on developer productivity and well-being find that the perceptions of productivity in a software team can be a socio-technical problem. Intuitively, problems and challenges can be better handled by managing expectations in software teams. Aim: Our goal is to understand whether the expectations of software developers vary towards diverse stakeholders in software teams. Method: We surveyed 181 professional software developers to understand their expectations from five different stakeholders: (1) organizations, (2) managers, (3) peers, (4) new hires, and (5) government and educational institutions. The five stakeholders are determined by conducting semi-formal interviews of software developers. We ask open-ended survey questions and analyze the responses using open coding. Results: We observed 18 multi-faceted expectations types. While some expectations are more specific to a stakeholder, other expectations are cross-cutting. For example, developers expect work-benefits from their organizations, but expect the adoption of standard software engineering (SE) practices from their organizations, peers, and new hires. Conclusion: Out of the 18 categories, three categories are related to career growth. This observation supports previous research that happiness cannot be assured by simply offering more money or a promotion. Among the most number of responses, we find expectations from educational institutions to offer relevant teaching and from governments to improve job stability, which indicate the increasingly important roles of these organizations to help software developers. This observation can be especially true during the COVID-19 pandemic.
In recent years, the World Economic Forum has identified software security as the most significant technological risk to the worlds population, as software-intensive systems process critical data and provide critical services. This raises the question of the extent to which German companies are addressing software security in developing and operating their software products. This paper reports on the results of an extensive study among developers, product owners, and managers to answer this question. Our results show that ensuring security is a multi-faceted challenge for companies, involving low awareness, inaccurate self-assessment, and a lack of competence on the topic of secure software development among all stakeholders. The current situation in software development is therefore detrimental to the security of software products in the medium and long term.
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