Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

Geist, Verena; Moser, M.; Pichler, Josef; Santos, Raquel; Wieser, Volkmar

doi:10.1002/spe.2933

Cited by 6 publications

(3 citation statements)

References 31 publications

(68 reference statements)

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“…AI is an area of computer science, and it is concerned with the process of analyzing large amounts of data. Machine learning 52 is an application or subset of AI that enables our algorithms to automatically develop and adapt expertise without even being overtly programmed. Keeping all this in observance, a hybrid classification approach is proposed in this section, where the integration of machine learning and heuristic analysis is exemplified.…”

Section: Proposed Methodologymentioning

confidence: 99%

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

Marimuthu¹,

Gopalasamy²,

Ben‐Othman

2021

Softw Pract Exp

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In the inevitably hi‐tech universe of cybercrimes, one of the major still prevailing methods is the usage of the malicious URLs and creating a Phishing link to obtain user credentials from the people. This method is highly subtle and has more effect on people's lives as well as corporate loss. To identify the malicious URLs, the security community has listed blacklists and benign links online. Still, as the technology is being developed day by day, the attackers try to create new phishing URLs using new social engineering methods that could be easily forged into the user's account. To improve the generality of malicious URL detectors, machine‐learning techniques have been explored with increasing attention in recent years. This article addresses the detection of malicious URLs combining the intelligence of both the heuristic‐based method and the machine learning process. It has been found that there are many possibilities for detecting zero‐day attacks and spear‐phishing attacks when incorporating both lexical features and machine learning methods. Out of the six‐batch learning process analyzed, we implement a decision tree algorithm in our framework with 99.47% accuracy during evaluation. The true positive values gained in our proposed hybrid framework is 99.2% and indicate <1% of the false‐positive values. The implementation shows a precision level higher than the previous model developed and by other antiphishing techniques. A high detection rate on zero‐day and spear‐phishing attacks and overall results reveal that our system outclasses the current approach to detecting phishing scams.

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Section: Proposed Methodologymentioning

confidence: 99%

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

Marimuthu¹,

Gopalasamy²,

Ben‐Othman

2021

Softw Pract Exp

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“…It is crucial to assess whether these stakeholders understand the query responses provided by the trained model. User guidelines around functions, business rules and domain concepts related to ML models can potentially be generated automatically [102] to provide end-users with explanations for the functionalities, operational mechanisms and restrictions that drive an ML solution.…”

Section: Deploymentmentioning

confidence: 99%

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

Khuat¹,

Kedziora²,

Gabrys³

2022

Preprint

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As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the 'how' and 'why' of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) How does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve? To consider these questions, we project existing literature in HCI into the space of AutoML; this connection has, to date, largely been unexplored. In so doing, we review topics including user-interface design, human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, we contemplate how AutoML may manifest in effectively open-ended environments. This discussion necessarily reviews projected developmental pathways for AutoML, such as the incorporation of reasoning, although the focus remains on how and why HCI may occur in such a framework rather than on any implementational details. Ultimately, this review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.

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“…Chittimalli et al 30 reported on a more ambitious goal: a language‐independent framework to extract the rules, 31 to create new ones, to verify them using the logs, and to analyze them. Geist et al 32 were able to extract business rules from source code statements with the aim of generating documentation for software tools automatically. Saruwatari et al, 33 highlighted the difficulty to extract business rules from general purpose programming languages and suggested to analyze the execution logs instead.…”

Section: Related Workmentioning

confidence: 99%

On the design of an advanced business rule engine

Jiménez

Corchuelo

2022

Softw Pract Exp

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Business rules govern how well‐managed companies perform every day. They are expected to be written in natural language because they are devised by business people. That makes it difficult to translate them automatically into executable rules that can be integrated into typical information systems. Our industrial and academic research concludes that the current tools have a number of problems, namely: many of them do not pay any attention to the SBVR standard; some of them are not multi‐language; most of them cannot achieve perfect parsing accuracy; some of them are not domain agnostic; some of them use proprietary technologies; some of them do not produce executable rules; and none supports exploratory what‐if analyses natively. In this article, we present Tier‐Rules, which is a system that overcomes the previous problems. We also report on an industrial case study that helps illustrate it in practice.

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Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

Cited by 6 publications

References 31 publications

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

The Roles and Modes of Human Interactions with Automated Machine Learning Systems

On the design of an advanced business rule engine

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