Human Error Analysis in Software Engineering

Huang, Fuqun

doi:10.5772/intechopen.68392

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…Known causes of human error from a software developer perspective such as fatigue, cognitive overload, attention slips, and stress have not been much addressed in software engineering research. All these cognitive states (high mental effort, stress level, attention shifts, cognitive overload, mental fatigue) have been associated with error-prone scenarios, as established by human error models [ 10 ] and their adaptation to software development tasks [ 11 , 12 ], but so far none of the available software development methodologies uses any form of direct information from the cognitive state of the software developer.…”

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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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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“…Huang (one of the present authors) and Liu [55] first proposed the interdisciplinary area ''Software Fault Defense based on Human Errors'' [55], [56], which aims to systematically reduce software defects based on an understanding of the cognitive errors of software practitioners. After that, Huang et al have conducted a series of in-depth studies on various topics: defect prevention based on human errors [9], [57], which includes a comprehensive human error taxonomy for root cause analysis [9], and an approach for promoting software developers' knowledge, awareness and abilities to prevent defects through cognitive training [57]; fault tolerance based on human errors [58], which identified several dimensions of cognitive styles and performance levels that can be used to seek fault diversity; the new controlled experimental method for studying the cause-effect relationships between the cognitive error mechanisms of software engineers and software defects [59]; human error modeling tool, which is used to represent the interactions between human errors and various context factors [60]; code review based on human errors [61], in which code reviewers' performance in finding defects in code is improved through ''cognitive training''.…”

Section: B Human Error Cause Of Software Defectsmentioning

confidence: 99%

“…Though human errors appear to be diverse across different activities, a proportion of human errors are predictable, in the sense that human errors take a limited number of recognizable patterns [59]. The proposed approach HEDF, is therefore based on analyzing a programming task for patterns that match the conditions known to trigger erroneous human behaviors; patterns that psychologists have observed to recur across diverse activities [59], [60]. We call this Error-Prone Scenario Analysis (EPSA).…”

Section: The Proposed Approachmentioning

confidence: 99%

HEDF: A Method for Early Forecasting Software Defects Based on Human Error Mechanisms

Huang

Strigini

2023

IEEE Access

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As the primary cause of software defects, human error is the key to understanding, and perhaps to forecasting and avoiding defects. Little research has been done to forecast defects on the basis of the cognitive errors that cause them. The existing ''defect prediction'' models are applied to code once it has been produced: therefore, their ''predictions'' have little implications for preventing the defects. This paper proposes an approach, ''Human-Error-based Defect Forecast'' (HEDF), to forecasting the exact defects at early stages of software development, before the code is produced, through knowledge about the cognitive mechanisms that cause developers' errors. This approach is based on a model of human error mechanisms underlying software defects: a defect is caused by an error-prone scenario triggering human error modes, which psychologists have observed to recur across diverse activities. Software defects can then be forecast by identifying such error-prone scenarios the in requirements and/or design documents. We assessed this approach empirically, with 55 programmers in a programming competition and four representative analysts serving as the users of the approach. Impressively, the approach was able to forecast, at the requirement phase, 75.7% of the defects later committed by all of the programmers. When considering just the defect forms, which may manifest as distinct defects even in the same program, the proposed method predicted 31.8% of them. This approach substantially improved the defect forecasting performances for analysts of various expertise, with a minimum of 100% improvement, compared to forecasts without the approach. If the forecast had been used to prevent the defects, it could have saved an estimated 46.2% of the debugging effort and increased the fraction of programmers delivering an acceptable program by 32.6%. The observed excellent performance of HEDF in forecasting (early at requirement stage) the exact forms and locations of defects that may be later introduced by developers into code makes it a promising candidate for preventing the defects, worthy of further study.

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“…These peculiarities, together with typical human error, make the development and integration process error-prone. Smart contracts therefore need to be designed using best practices that reduce the number of coding and operational errors [7].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Smart Contract Meta-modeling

Sánchez-Gómez

Torres-Valderrama

Risoto

et al. 2021

JWE

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One of the key benefits of blockchain technology is its ability to keep a permanent, unalterable record of transactions. In business environments, where companies interact with each other without a centralized authority to ensure trust between them, this has led to blockchain platforms and smart contracts being proposed as a means of implementing trustworthy collaborative processes. Software engineers must deal with them to ensure the quality of smart contracts in all phases of the smart contract lifecycle, from requirements specifications to design and deployment. This broad scope and criticality of smart contracts in business environments means that they have to be expressed in a language that is intuitive, easy-to-use, independent of the blockchain platform employed, and oriented towards software quality assurance. In this paper we present a key component: a first outline of a UML-based smart contract meta-model that would allow us to achieve these objectives. This meta-model will be enriched in future work to represent blockchain environments and automated testing.

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Human Error Analysis in Software Engineering

Cited by 13 publications

References 16 publications

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

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

HEDF: A Method for Early Forecasting Software Defects Based on Human Error Mechanisms

Blockchain Smart Contract Meta-modeling

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