New approach of the Customer Defects per Lines of Code metric in Automotive SW Development applications

Sandu, Ionut-Andrei; Sălceanu, Alexandru; Bejenaru, Ovidiu

doi:10.1088/1742-6596/1065/5/052006

Cited by 10 publications

(10 citation statements)

References 2 publications

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“…This problem is amplified by the constant pressure to minimize the time-to-market and due to the dramatic increase in code size witnessed by modern software. More lines of code (LoC) mean more bugs, as attested by the fact that the number of LoC is generally used as the most reliable metric to predict bug count in software products [5]. Knowing the high impact of software in our society, software bugs represent the most chronic and challenging problem, which might cause considerable negative consequences on the final product/service resulting from the software.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Hijazi

Duraes

Couceiro

et al. 2023

IIEEE Trans. Software Eng.

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Code review is an essential practice in software engineering to spot code defects in the early stages of software development. Modern code reviews (e.g., acceptance or rejection of pull requests with Git) have become less formal than classic Fagan's inspections, lightweight, and more reliant on individuals (i.e., reviewers). However, reviewers may encounter mentally demanding challenges during the code review, such as code comprehension difficulties or distractions that might affect the code review quality. This work proposes a novel approach that evaluates the quality of code reviews in terms of bug-finding effectiveness and provides the reviewers with a clear message of whether the review should be repeated, indicating the code regions that may not have been well-reviewed. The proposed approach utilizes biometric information collected from the reviewer during the review process using non-intrusive biofeedback devices (e.g., smartwatches). Biometric measures such as Heart Rate Variability (HRV) and task-evoked pupillary response are captured as a surrogate of the cognitive state of the reviewer (e.g., mental workload) and inexpensive desktop eye-trackers compatible with the software development settings. This work uses Artificial Intelligence techniques to predict the cognitive load from the extracted biomarkers and classify each code region according to a set of features. The final evaluation considers various factors such as code complexity, time of the code review, the experience level of the reviewer, and other factors. Our experimental results show the approach could predict the review quality with 87.77%±4.65 accuracy and a Spearman correlation coefficient of 0.85 (p-value < 0.001) between the predicted and the actual review performance. This evaluation validates the cognitive load measurement using electroencephalography (EEG) signals as ground truth for the HRV and pupil signals.

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Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Hijazi

Duraes

Couceiro

et al. 2023

IIEEE Trans. Software Eng.

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“…Even when software is developed using highly mature processes, the deployed code still has a relatively high defect density, from 1 to 5 bugs per KLoC [ 2 , 3 , 4 , 5 ]. More recent field data from several projects show defects densities from 1 to 6 bugs per KLoC [ 6 ], suggesting that in real projects, often characterized by millions of lines of code, the defect density remains quite significant and represents a huge cost for the software industry and society in general. Subsequently, reports on the finance impact of software defects and software failures show dramatic figures.…”

Section: Introductionmentioning

confidence: 99%

Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load?

Medeiros

Couceiro

Duarte

et al. 2021

Sensors

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An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers’ cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers’ cognitive load associated with the comprehension of code with different complexity levels. Therefore, a controlled experiment involving 26 programmers was carried. We found that features related to Theta, Alpha, and Beta brain waves have the highest discriminative power, allowing the identification of code lines and demanding higher mental effort. The EEG results reveal evidence of mental effort saturation as code complexity increases. Conversely, the classic software complexity metrics do not accurately represent the mental effort involved in code comprehension. Finally, EEG is proposed as a reference, in particular, the combination of EEG with eye tracking information allows for an accurate identification of code lines that correspond to peaks of cognitive load, providing a reference to help in the future evaluation of the space and time accuracy of programmers’ cognitive state monitored using wearable devices compatible with software development activities.

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“…However, in spite of decades of research and advances in software engineering and software reliability, software defects (i.e., bugs) remain as the most enduring problem of software quality [3][4][5]. The average number of bugs per 1000 lines of delivered code (KLOC) [6] remains astonishingly high, which reinforces the importance of understanding error-monitoring processes in the brain during execution of this novel complex task. This might have important implications for understanding how the brain can control programming performance [7].…”

Section: Introductionmentioning

confidence: 99%

Reading and Calculation Neural Systems and Their Weighted Adaptive Use for Programming Skills

et al. 2021

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Software programming is a modern activity that poses strong challenges to the human brain. The neural mechanisms that support this novel cognitive faculty are still unknown. On the other hand, reading and calculation abilities represent slightly less recent human activities, in which neural correlates are relatively well understood. We hypothesize that calculus and reading brain networks provide joint underpinnings with distinctly weighted contributions which concern programming tasks, in particular concerning error identification. Based on a meta-analysis of the core regions involved in both reading and math and recent experimental evidence on the neural basis of programming tasks, we provide a theoretical account that integrates the role of these networks in program understanding. In this connectivity-based framework, error-monitoring processing regions in the frontal cortex influence the insula, which is a pivotal hub within the salience network, leading into efficient causal modulation of parietal networks involved in reading and mathematical operations. The core role of the anterior insula and anterior midcingulate cortex is illuminated by their relation to performance in error processing and novelty. The larger similarity that we observed between the networks underlying calculus and programming skills does not exclude a more limited but clear overlap with the reading network, albeit with differences in hemispheric lateralization when compared with prose reading. Future work should further elucidate whether other features of computer program understanding also use distinct weights of phylogenetically “older systems” for this recent human activity, based on the adjusting influence of fronto-insular networks. By unraveling the neural correlates of program understanding and bug detection, this work provides a framework to understand error monitoring in this novel complex faculty.

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New approach of the Customer Defects per Lines of Code metric in Automotive SW Development applications

Cited by 10 publications

References 2 publications

Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load?

Reading and Calculation Neural Systems and Their Weighted Adaptive Use for Programming Skills

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