Pub Date : 2023-12-01DOI: 10.1142/s0218194023430015
Fernando Ibarra-Torres, Matias Urbieta, N. Medina-Medina
{"title":"Appling Scrum to knowledge transfer among software developers","authors":"Fernando Ibarra-Torres, Matias Urbieta, N. Medina-Medina","doi":"10.1142/s0218194023430015","DOIUrl":"https://doi.org/10.1142/s0218194023430015","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138626014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.1142/s0218194023500675
Yang Qu, Jinchen Xu, Bei Zhou, Jiangwei Hao, Fei Li, Zuoyan Zhang
{"title":"SCR-LIBM: A Correctly Rounded Elementary Function Library in Double-Precision","authors":"Yang Qu, Jinchen Xu, Bei Zhou, Jiangwei Hao, Fei Li, Zuoyan Zhang","doi":"10.1142/s0218194023500675","DOIUrl":"https://doi.org/10.1142/s0218194023500675","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-08DOI: 10.1142/s0218194023500596
Yanfang Ma, Wei Zhou
During the process of software development, a significant challenge revolves around accurately estimating the associated costs. The primary goal of project managers is to ensure the delivery of a highly trustworthiness product that aligns with the designated budgetary constraints. Nonetheless, the trustworthiness of software hinges upon a range of distinct attributes. When implementing a budget allocation scheme to enhance these attributes, conflicts among them may arise. Thus, it becomes imperative to select an appropriate allocation scheme that effectively mitigates conflict-associated costs. In this paper, we will define the conflict costs and establish costs estimation models. The difficulty coefficient constraint for improving attributes is established. Subsequently, we will analyze the relative importance weights of these attributes. Drawing upon the conflict costs, importance weights, and difficulty coefficient constraint, we present an algorithm to determine an appropriate budget allocation scheme, which can minimize conflict-associated costs. Finally, we provide an illustrative example that demonstrates the practicability of our proposed algorithm. This research offers valuable insights to software managers, aiding them in the reasonable allocation of budgetary resources, thereby maximizing overall benefits.
{"title":"The Allocation Scheme of Software Development Budget with Minimal Conflict Attributes","authors":"Yanfang Ma, Wei Zhou","doi":"10.1142/s0218194023500596","DOIUrl":"https://doi.org/10.1142/s0218194023500596","url":null,"abstract":"During the process of software development, a significant challenge revolves around accurately estimating the associated costs. The primary goal of project managers is to ensure the delivery of a highly trustworthiness product that aligns with the designated budgetary constraints. Nonetheless, the trustworthiness of software hinges upon a range of distinct attributes. When implementing a budget allocation scheme to enhance these attributes, conflicts among them may arise. Thus, it becomes imperative to select an appropriate allocation scheme that effectively mitigates conflict-associated costs. In this paper, we will define the conflict costs and establish costs estimation models. The difficulty coefficient constraint for improving attributes is established. Subsequently, we will analyze the relative importance weights of these attributes. Drawing upon the conflict costs, importance weights, and difficulty coefficient constraint, we present an algorithm to determine an appropriate budget allocation scheme, which can minimize conflict-associated costs. Finally, we provide an illustrative example that demonstrates the practicability of our proposed algorithm. This research offers valuable insights to software managers, aiding them in the reasonable allocation of budgetary resources, thereby maximizing overall benefits.","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135293547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-04DOI: 10.1142/s0218194023500572
Yi Zhu, Yuxiang Gao, Yu Qiao
Interpretation is important for adopting software defect prediction in practice. Model-agnostic techniques such as Local Interpretable Model-agnostic Explanation (LIME) can help practitioners understand the factors which contribute to the prediction. They are effective and useful for models constructed on tabular data with traditional features. However, when they are applied on source code-based models, they cannot differentiate the contribution of code tokens in different locations for deep learning-based models with Bag-of-Word features. Besides, only using limited features as explanation may result in information loss about actual riskiness. Such limitations may lead to inaccurate explanation for source code-based models, and make model-agnostic techniques not useful and helpful as expected. Thus, we apply a perturbation-based approach Randomized Input Sampling Explanation (RISE) for source code-based defect prediction. Besides, to fill the gap that there lacks a systematical evaluation on model-agnostic techniques on source code-based defect models, we also conduct an extensive case study on the model-agnostic techniques on both token frequency-based and deep learning-based models. We find that (1) model-agnostic techniques are effective to identify the most important code tokens for an individual prediction and predict defective lines based on the importance scores, (2) using limited features (code tokens) for explanation may result in information loss about actual riskiness, and (3) RISE is more effective than others as it can generate more accurate explanation, achieve better cost-effectiveness for line-level prediction, and result in less information loss about actual riskiness. Based on such findings, we suggest that model-agnostic techniques can be a supplement to file-level source code-based defect models, while such explanations should be used with caution as actual risky tokens may be ignored. Also, compared with LIME, we would recommend RISE for a more effective explanation.
{"title":"An Empirical Study on Model-Agnostic Techniques for Source Code-Based Defect Prediction","authors":"Yi Zhu, Yuxiang Gao, Yu Qiao","doi":"10.1142/s0218194023500572","DOIUrl":"https://doi.org/10.1142/s0218194023500572","url":null,"abstract":"Interpretation is important for adopting software defect prediction in practice. Model-agnostic techniques such as Local Interpretable Model-agnostic Explanation (LIME) can help practitioners understand the factors which contribute to the prediction. They are effective and useful for models constructed on tabular data with traditional features. However, when they are applied on source code-based models, they cannot differentiate the contribution of code tokens in different locations for deep learning-based models with Bag-of-Word features. Besides, only using limited features as explanation may result in information loss about actual riskiness. Such limitations may lead to inaccurate explanation for source code-based models, and make model-agnostic techniques not useful and helpful as expected. Thus, we apply a perturbation-based approach Randomized Input Sampling Explanation (RISE) for source code-based defect prediction. Besides, to fill the gap that there lacks a systematical evaluation on model-agnostic techniques on source code-based defect models, we also conduct an extensive case study on the model-agnostic techniques on both token frequency-based and deep learning-based models. We find that (1) model-agnostic techniques are effective to identify the most important code tokens for an individual prediction and predict defective lines based on the importance scores, (2) using limited features (code tokens) for explanation may result in information loss about actual riskiness, and (3) RISE is more effective than others as it can generate more accurate explanation, achieve better cost-effectiveness for line-level prediction, and result in less information loss about actual riskiness. Based on such findings, we suggest that model-agnostic techniques can be a supplement to file-level source code-based defect models, while such explanations should be used with caution as actual risky tokens may be ignored. Also, compared with LIME, we would recommend RISE for a more effective explanation.","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135728296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1142/s021819402350064x
Marko Pozenel, Luka Furst, Damjan Vavpotic, Tomaz Hovelja
{"title":"Agile Effort Estimation: Comparing the Accuracy and Efficiency of Planning Poker, Bucket System, and Affinity Estimation methods","authors":"Marko Pozenel, Luka Furst, Damjan Vavpotic, Tomaz Hovelja","doi":"10.1142/s021819402350064x","DOIUrl":"https://doi.org/10.1142/s021819402350064x","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1142/s0218194023410073
Shunwen Shen, Mulan Yang, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang
{"title":"DeepMultiple: A Deep Learning Model for RFID-based Multi-object Activity Recognition","authors":"Shunwen Shen, Mulan Yang, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang","doi":"10.1142/s0218194023410073","DOIUrl":"https://doi.org/10.1142/s0218194023410073","url":null,"abstract":"","PeriodicalId":50288,"journal":{"name":"International Journal of Software Engineering and Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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