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Dohi, Tadashi; Nishio, Yasuhiko; Osaki, Shunji

doi:10.1023/a:1018962910992

Cited by 61 publications

(4 citation statements)

References 45 publications

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“…Dealing with such problems which are accompanied by many vague factors (in terms of quantity evaluation), the majority of experts would select neural networks as an appropriate approach because these networks are capable of estimating complicated functions quite well. Therefore, the neural networks have been paid more attention than two other techniques in this field, and many papers have been presented on this approach so far [9][10][11][12][13][14][15]. The neural networks were first presented in [14], [15] for this field.…”

Section: Methods Based On Neural Networkmentioning

confidence: 99%

Taxonomy of intelligence software reliability model

Ahmadluei

2015

JACST

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The probability of failure free software operation for a specified period of time in a specified environment is called Reliability, it is one of the attributes of software quality and study about it come back to 1384. Exposition and spreading of new software systems and profound effect of it to human life emphasize the importance of software reliability analysis, until it poses formal definition at 1975. First race of reliability analysis methods that we called classic methods has stochastic process approach and in this way, attempt to predict the software behavior in future. Due to the ambiguity in fruitfulness of these solutions the challenge about reliability analysis continued till now. Great tendency in applying intelligence systems at variety of applications can be seen at 90 decade, and software reliability attracts some research direction to itself. Until now variety of methods in reliability analysis on the base of intelligence systems approach exhibited. In this survey the taxonomy of these methods represented with brief description of each one. Also comparison between these methods can be seen at the end of survey.

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Section: Methods Based On Neural Networkmentioning

confidence: 99%

Taxonomy of intelligence software reliability model

Ahmadluei

2015

JACST

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“…Machine learning has percolated throughout the SE industry leading to many uses of datadriven algorithms for traditional SE activities [58]. ML's ability to predict and estimate can be very beneficial, for example when predicting the number of faults when given software metrics as input [59], or to estimate software size [60], predict software cost [61], estimating software correction costs [62], predicting software reliability [63], defect prediction [64], software release timing [65] or when estimating the development effort [66]. ML can be beneficial when applied to property and model discovery, such as when evaluating process models [67].…”

Section: Machine Learning In Software Engineeringmentioning

confidence: 99%

Discovering Related Terms and Detecting Trends in Software Engineering Using Word Embeddings

Baskararajah

2024

Preprint

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<p>The Software Engineering (SE) community is prolific, making it challenging for experts to keep up with the flood of new papers and for neophytes to enter the field. One solution that has been proposed to ease the burden of entry on the community would be automatic summarization of papers. While there exist term and trend summarization and analysis tools, the unique language utilized in SE requires bespoke solutions. Therefore, we posit that the community may benefit from a tool extracting terms and their interrelations from the SE community's text corpus and showing terms' trends. In this paper, we build a prototyping tool using the word embedding technique. We train the embeddings on the SE Body of Knowledge handbook and 15,233 research papers' titles and abstracts. We create test cases necessary for validation of the training of the embeddings. Upon gathering the trends of interrelated SE terms, we also use cluster analysis to investigate the trends, to help discover underlying patterns in the way trends in SE rise and fall in popularity. We provide representative examples showing that the embeddings may aid in summarizing terms and uncovering trends in the knowledge base, as well as showing examples of patterns that may lie underneath trends in interrelated terms in software engineering.</p>

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“…Yamada and Osaki [12] once introduced a decision-making model that concurrently evaluates reliability and cost. Additionally, Dohi [13] simplified the cost minimization issue to a time series forecasting problem, which was addressed using artificial neural networks. Nishio and Dohi [14] also harnessed Cox's proportional hazards model to address the software reliability evaluation.…”

Section: Background and Related Workmentioning

confidence: 99%

Utilizing Multi-Objective Evolutionary Algorithms to Optimize Open Source Software Release Management

Yu,

Huang

2023

IEEE Access

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Scheduling and resource allocation for Open Source Software (OSS) product development pose crucial and challenging tasks due to program size and resource limitations. The properties of OSS further complicate product assessment and maintenance for developers. This paper proposes a model for the iterative and multi-release OSS development process. Unlike traditional methods that oversimplify the problem by reducing the multi-decision space into a single-objective optimization problem, our approach suggests employing Multi-objective Evolutionary Algorithms (MOEAs) to solve the Optimal Release Time Planning Problem, enabling the simultaneous maximization of reliability and minimization of cost. We consider testing cost and system reliability, two critical dimensions, as the primary objectives, while also incorporating testing resource consumption as the third objective. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is chosen as the primary method for its effectiveness in MOEAs, with a special scenario outlined in the paper where NSGA-II may not guarantee optimal solutions. Demonstrating the practicality of our proposed method, we utilize open source data to assess release time and illustrate its superiority to NSGA-II. Numerical examples further showcase the model's effectiveness.

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Untitled

Cited by 61 publications

References 45 publications

Taxonomy of intelligence software reliability model

Taxonomy of intelligence software reliability model

Discovering Related Terms and Detecting Trends in Software Engineering Using Word Embeddings

Utilizing Multi-Objective Evolutionary Algorithms to Optimize Open Source Software Release Management

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