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Applying Machine Learning to Estimate the Effort and Duration of Individual Tasks in software Projects

IEEE ACCESS 2023年 11卷 89933-89946页

作者： Sousa, Andre Veloso, Daniel Goncalves, Henrique Faria, Joao Pascoal Mendes-Moreira, Joao Graca, Ricardo Gomes, Duarte Castro, Rui Nuno Henriques, Pedro Castro Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal Fraunhofer Portugal AICOS P-4200135 Porto Portugal Strongstep P-4100429 Porto Portugal

software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to erro

关键词： Task analysis software software algorithms Maximum likelihood estimation Prediction algorithms Predictive models Machine learning algorithms software development management Project management Effort estimation duration estimation machine learning task estimation software projects

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A Survey on algorithms and software for the Frequent Itemset Hiding Problem

A Survey on Algorithms and Software for the Frequent Itemset...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Panteleimon Krasadakis Vassilios S. Verykios Evangelos Sakkopoulos Department of Informatics University of Piraeus Piraeus Greece School of Sciences and Technology Hellenic Open University Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Privacy preserving data mining is an increasingly popular field of research with many problems being actively researched in this domain. Specifically, the Frequent Itemset Hiding Problem (FIHP) poses significant challenges in data mining, particularly concerning the privacy-preserving extraction of frequent itemsets from sensitive datasets. This survey paper comprehensively explores the landscape of software solutions dedicated to addressing the FIHP. Through an extensive review of existing methodologies, algorithms, and implementations, this survey provides a systematic analysis of the state-of-the-art in FIHP software. The survey categorizes and evaluates various approaches, ranging from traditional Apriori-based algorithms to recent advancements leveraging graph-based techniques and machine learning. By synthesizing the existing literature and high-lighting emerging trends, this survey aims to guide researchers and practitioners in selecting suitable software solutions for tackling the FIHP in diverse data mining applications.

关键词： Surveys Data privacy Machine learning algorithms Systematics Itemsets Reviews software algorithms Machine learning Market research software

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Heterogeneous Ensemble Model to Optimize software Effort Estimation Accuracy

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IEEE ACCESS 2023年 11卷 27759-27792页

作者： Ali, Syed Sarmad Ren, Jian Zhang, Kui Wu, Ji Liu, Chao Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China Mohammad Ali Jinnah Univ Dept Comp Sci Karachi 75400 Pakistan Natl Univ Comp & Emerging Sci FAST Dept Comp Sci Islamabad 44000 Pakistan

The software industry has experienced rapid expansion in recent years, with software development now essential to the success of many multinational corporations. The demand for complex software systems has dramatically increased, effective software development has become crucial, given the limitations of resources such as money, time, and labor. Cost and effort calculations significantly impact the development process and client needs, and project failure is often caused by errors in job estimating. Underestimating a project's cost and effort can have severe repercussions, such as exceeding the project's budget. Project overruns, on the other hand, can also have a detrimental impact on software projects' successful completion. Researchers and experts in the software industry are continually exploring ways to keep management and development productivity at high levels. However, standalone estimating models have revealed inadequacies over the last decade, and they have not produced any noteworthy research results. Recent literature suggests that opting for ensemble models would yield better results than standalone models. We have proposed a heterogeneous ensemble effort estimation (EEE) model in this research. Our proposed model comprises standalone estimating models such as Use Case Point, Expert Judgment (EJ), and Artificial Neural Network (ANN). We combined the effort of each unique base model using linear combination rule. To validate our model's effectiveness, we applied it to the benchmark dataset, the International software Benchmarking Standards Group (ISBSG), using three different variations to avoid biases. We further applied the trained models to industry use cases for cross-validation. Our study's findings demonstrated that, in comparison to stand-alone estimate strategies, the ensemble technique produced better estimation results. Finally, our study proposes a heterogeneous ensemble effort estimation model that outperforms standalone models in terms of ac

关键词： software Estimation Costs Artificial neural networks software algorithms Predictive models Machine learning algorithms software effort estimation ensemble effort estimation (EEE) standalone estimation use case point (UCP) machine learning algorithms deep learning expert judgement

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Evolutionary Algorithm for software Project Scheduling Considering Team Relationships

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IEEE ACCESS 2023年 11卷 43690-43706页

作者： Zhang, Jianhao Shen, Xiaoning Yao, Chengbin Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Jiangsu Peoples R China Jiangsu Key Lab Big Data Anal Technol Nanjing 210044 Jiangsu Peoples R China Jiangsu Engn Res Ctr Meteorol Energy Using & Contr Nanjing 210044 Jiangsu Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Jiangsu Peoples R China

Human relationships have a great impact on the software development process. Meanwhile, project development may require the acquisition of new skills that employees have not yet acquired due to changes in customer requirements. However, such factors are seldom incorporated into software project scheduling problem. To address this, a novel mathematical model considering the team relationships is established for software project scheduling problem. This model introduces the communication cost factor of each employee, and analyses its quantitative connections with changes in human relationships and the growth of skill proficiency respectively. In addition, a selection mechanism for external employees is designed to meet new skill requirements. To solve the model, an improved bi-population discrete evolutionary algorithm based on information feedback is proposed. The heuristic information of "employee suitability" is utilized in initialization to obtain better initial individuals. The feedback mechanism based on "evolutionary quality" is applied to provide an effective strategy for adaptive tuning of subpopulation size. The selection probabilities of distinct crossover operators are adjusted according to the "improved quantity" to increase crossover efficiency. Moreover, an enhanced local search strategy based on the "degree of team cooperation" and the "improved quantity" is developed. Experimental results show that by considering human relationships under different communication cost factors, the duration and cost of the project are significantly reduced. Compared with four state-of-the-art algorithms, the scheduling performance of the proposed algorithm can be improved by 10.6374% to 25.8566%.

关键词： software Task analysis Costs software algorithms Evolutionary computation Optimization Optimal scheduling software project scheduling communication cost human relationship information feedback evolutionary algorithm

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Balanced Domain Partitioning for software Defined Networks

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IEEE ACCESS 2023年 11卷 6467-6476页

作者： Wu, Yuansheng Du, Junyi Ni, Dadong Southwest China Inst Elect Technol Chengdu 610036 Peoples R China

software Defined Networking (SDN) has been widely adopted to enable the network programmability by decoupling the controller logic from the data plane. Domain partitioning is essential for SDN-enabled communication networks to partition the oversized network topology into controllable and administrative domains. Existing domain partitioning algorithms failed to take the connectivity, balance constraint, compactness, and traffic cohesion into consideration simultaneously which are required by SDN communications. This paper formulates the domain partitioning problem and proposed a novel balanced network partitioning (BaDoP) algorithm, which can enhance the balance of the partitioned domains, and optimize the compactness and traffic cohesion with connectivity constraint always guaranteed. BaDoP employs Bubble algorithm to find initial domain partitioning, and improves the domain partitioning iteratively by moving border nodes among the partitioned domains guided by the negative cycles or shortest paths on an auxiliary graph which is an abstract view of the current domain partitioning. The simulation results indicates that our BaDoP algorithm outperforms the previous algorithms and can achieve significant improvement for the balance of the partitioned domains.

关键词： Partitioning algorithms Communication networks Clustering algorithms software algorithms Network topology software defined networking Iterative algorithms Auxiliary graph balanced domain partitioning compactness connectivity constraint negative cycle detection software-defined networking traffic cohesion

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Toward an Analysis Method of software Evolution Triggered by Place Change in Mobile Computing

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IEEE TRANSACTIONS ON RELIABILITY 2023年第2期72卷 527-541页

作者： Lu, Chao-Ze Zeng, Guo-Sun Tongji Univ Dept Comp Sci & Technol Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 201804 Peoples R China Ningbo Univ Technol Sch Cyber Sci & Engn Ningbo 315211 Peoples R China Tongji Univ Project Off Natl Seabed Sci Observat Syst Shanghai 201804 Peoples R China

software dynamic evolution is a hot topic in software engineering. Traditional evolution is caused by the change in user requirements and running environments. However, in mobile computing systems, there is a new problem that place change leads to the change in software configuration, deployment, and function, which is called the problem of software evolution triggered by place change. Facing this new problem, traditional evolution methods fail to formally describe their evolution process and analyze their evolution performance. Therefore, a new method needs to be developed to ensure the reliability of software evolution triggered by place change. We first address various situations regarding the change of the software function caused by place change in physical space, which are described by a Bigraph model. Then, the triggering conditions for software evolution are defined, and a reaction system corresponding to software evolution is discussed. A theory about software function change caused by place change is developed, including a novel algorithm for identifying software evolution rules that involve place information and the control process for the whole software evolution (CPSE) in a mobile computing environment. Finally, some real case studies illustrate the effectiveness and correctness of our proposed method. Extensive experiments show that our algorithm CPSE outperforms Quiescence and version consistency (VC)-concurrent versions (CV) algorithms in timeliness and average disruption.

关键词： software Switches software systems Mobile computing Unified modeling language Computational modeling software algorithms Bigraph model evolution reliability place change software architecture software evolution

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Fast Parametric Model Checking With Applications to software Performability Analysis

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IEEE TRANSACTIONS ON software ENGINEERING 2023年第10期49卷 4707-4730页

作者： Fang, Xinwei Calinescu, Radu Gerasimou, Simos Alhwikem, Faisal Univ York Dept Comp Sci York YO10 5GH England Qassim Univ Coll Comp Dept Comp Sci Buraydah 51452 Saudi Arabia

We present an efficient parametric model checking technique for the analysis of software performability, i.e., of the performance and dependability properties of software systems. The new parametric model checking (pMC) technique works by using a heuristic to automatically decompose a parametric discrete-time Markov chain (pDTMC) model of the software system under verification into fragments that can be analysed independently, yielding results that are then combined to establish the required software performability properties. Our fast parametric model checking (fPMC) technique enables the formal analysis of software systems modelled by pDTMCs that are too complex to be handled by existing pMC methods. Furthermore, for many pDTMCs that state-of-the-art parametric model checkers can analyse, fPMC produces solutions (i.e., algebraic formulae) that are simpler and much faster to evaluate. We show experimentally that adding fPMC to the existing repertoire of pMC methods improves the efficiency of parametric model checking significantly, and extends its applicability to software systems with more complex behaviour than currently possible.

关键词： Markov processes Parametric statistics software systems Analytical models Probabilistic logic Web servers software algorithms Parametric model checking software performability nonfunctional software properties Markov models

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Revisiting Scenarios of Using Refactoring Techniques to Improve software Systems Quality

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IEEE ACCESS 2023年 11卷 28800-28819页

作者： Almogahed, Abdullah Omar, Mazni Zakaria, Nur Haryani Muhammad, Ghulam AlQahtani, Salman A. Univ Utara Malaysia Sch Comp Sintok Malaysia King Saud Univ Coll Comp & Informat Sci Res Chair New Emerging Technol & Networks & Beyond Comp Engn Dept Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11543 Saudi Arabia

Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.

关键词： software measurement software systems software quality Code refractoring software algorithms Prediction algorithms software maintenance Factors quality attributes refactoring refactoring techniques scenarios of using refactoring techniques software metrics software quality software maintenance 5G

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Landlord: Coordinating Dynamic software Environments to Reduce Container Sprawl

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第5期34卷 1376-1389页

作者： Shaffer, Tim Phung, Thanh Son Chard, Kyle Thain, Douglas Univ Notre Dame Notre Dame IN 46556 USA Univ Chicago Chicago IL 60637 USA

Containers provide customizable software environments that are independent from the system on which they are deployed. Online services for task execution must often generate containers on the fly to meet user-generated requests. However, as the number of users grows and container environments are changed and updated over time, there is an explosion in the number of containers that must be managed, despite the fact that there is significant overlap among many of the containers in use. We analyze a trace of container launches on the public Binder service and demonstrate the performance and resource usage issues associated with container sprawl. We present Landlord, an algorithm that coalesces related container environments, and show that it can improve container reuse and reduce the number of container builds required in the Binder trace by 40%. We perform a sensitivity analysis of Landlord using randomized synthetic workloads on a high-energy physics (HEP) software repository and demonstrate that Landlord shows benefits for container management across a wide range of usage patterns. Finally, we compare Landlord to offline clustering, and observe that the continuous churn in software necessitates an online approach.

关键词： Containers Task analysis software Python software algorithms Sensitivity High performance computing Cluster computing containers file systems

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Multiplicative Weights algorithms for Parallel Automated software Repair 35

Multiplicative Weights Algorithms for Parallel Automated Sof...

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35th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Renzullo, Joseph Weimer, Westley Forrest, Stephanie Arizona State Univ Tempe AZ 85281 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665440660

Multiplicative Weights Update (MWU) algorithms are a form of online learning that is applied to multi-armed bandit problems. Such problems involve allocating a fixed number of trials among multiple options to maximize cumulative payoff. MWU is a popular and effective method for dynamically balancing the trade-off between exploring the value of new options and exploiting the information already gained. However, no clear strategy exists to help practitioners choose which of the several algorithmic designs within this family to deploy. In this paper, three variants of parallel MWU algorithms are considered: Two parallel variants that rely on global memory, and one variant that uses distributed memory. The three variants are first analyzed theoretically, and then their effectiveness is assessed empirically on the task of estimating distributions in the context of stochastic search for repairs to bugs in software. Earlier work on APR suffers from various inefficiencies, and the paper shows how to decompose the problem into two stages: one that is embarrassingly parallel and one that is amenable to MWU. We then model the cast of each MWU variant and derive the conditions under which it is likely to be preferred in practice. We find that all three MWU algorithms achieve accuracy above 90% but that there are significant differences in runtime and total cost. When 90% accuracy is sufficient and evaluating options is expensive, such as in our use case, we find that the algorithm that uses global memory and has high communication cast outperforms the other two. We analyze the reasons for this surprising result.

关键词： Distributed processing Runtime Heuristic algorithms software algorithms Computer bugs Stochastic processes Maintenance engineering

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