Improvement of Risk Identification of Software Projects using Clustering Approach

Favorable planed management is the base of software project succeed. In this piece aim at the present situation of software project management, comprehensive use the thought of software engineering and project management, explore process and method of project schedule planning control. On the base of analysis software project schedule planning control difficulties, research based on PSP method. PSP follow adjustment, practice, measurement, comparison result and readjustment such a circulation and the continuous improvement of process, it is a process step by step for a long time. This paper studies the successful application of content; it can change insufficient plan in software project management process, the phenomenon of control adverse, improve comprehensive management ability and competitive power of project, creating Value for software company, improve high quality service for user.

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In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (Cmip) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model's overfitting problem.

Software risk management refers to systematic process for analyzing and identifying the project risks. The present paper provides a hybrid method for IT software risks identification. Software projects possess different features which increase the project failure possibilities. Therefore, the present work integrate the Artificial Neural network with the Analytic Hierarchy Process (AHP-ANN) in order to solve the problem of software project estimation in early stage. The questionnaire developed to find out the risk functional model and provide the proposed method with proper data. The results observe a major common risk in software projects is the insufficient knowledge based on different software project life cycle stages. Also, there are some other important factors in software projects such as lack of good estimation in project scheduling, poor definition of project requirements which cause human errors.

Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy for each target domain sample in the source domain space. The proxy is supposed to be matched to the corresponding target domain sample convolutional representation vector well. To measure the matching quality, we proposed to maximize their squared-loss mutual information (SMI) between the proxy and target domain samples. We further develop a novel neural SMI estimator based on a parametric density ratio estimation function. Moreover, we also propose to minimize the classification error of both source domain samples and target domain proxies. The classification responses are also smoothened by manifolds of both the source domain and proxy space. By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. In this way, the proxy of a target domain sample can be matched to the source domain data and thus benefits from the rich supervision information of the source domain. We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.

The development effort estimation is one of the most difficult problems in software project management. It is one of the most critical aspects in the early stages of the software project. Several software development effort estimation models have been proposed, however, these models are not able to obtain more than a 25 percent of accuracy, neither provide an understandable model for experts in the application area. Therefore, in this paper, we present EEpred, an explanatory model to estimate the development effort based on data of known software projects. It is a serial multiple classifier system based-on several decision trees. The model performance was evaluated by an internal validation procedure, analyzing their robustness and predictive performance. This procedure demonstrates that EEpred is able to estimate the software development effort with a 71 percent of precision. The main advantage of EEpred, regarding to other algorithms, is its ability to translate the process into a collection of simple decision rules, providing more easily interpretable knowledge that can help software engineer to improve decision-making on development planning.

Software project risk analysis using Bayesian networks with causality constraints

Are software projects evaluated using software teams’ success criteria? A systematic literature review

Among critical stakeholders of software projects developed in public institutions are software developers and project managers affiliated to sub-contractor companies, who are working onsite at the public institutions. The aim of this study is to determine the effect of working onsite in public software projects for software developers and managers with respect to selected factors. The research is designed as an exploratory and qualitative study. The study participants were 10 software developers, one technical team leader and four project managers, all having long experiences in working on public software projects on client premises. Semi-structured interview technique is employed as the data collection method and “ analysis” was applied on the obtained participant responses. Following this analysis, “key ” approach was used to identify the issues to be further considered. It has been determined that the productivity of software developers working onsite at public institutions is low due to intense requests, shortened time to fulfill these requests, and long duration of authorization processes. Moreover, a finding of the study is that the quality of the developed projects and the motivation of the developers are considered to be low for the same aforementioned reasons. Among the foremost causes for these results are the fact that employees work under two different hierarchical structures one in the public institution and one in the software company, more than one managers assign work and there is an authority confusion between organizational units and people in the public institutions.

Software project management is the art and science of planning and leading software projects. It is a subdiscipline of project management in which software projects are planned, monitored and controlled. To ensure that a certain software project is successful is the main responsibility of the project manager. From the point of view of a project manager, a success is defined as: Did the system come in “up to specifications”, in time and on budget? Many factors come into play in deciding the success of the project such as project management, customer involvement etc. In this paper, online survey is used to collect project data about a number of factors upon which the outcome of the project depends. Data mining tools such as Association Rule Mining, Naive Bayes, Decision Tree and Neural networks are used to determine whether a project will be successful or not. In addition to that, it also finds out the most important factors guiding the project to success and determine the changes required to turn an unsuccessful project into a successful one.

Due to globalisation, advances in computing technologies, as well as the deployment of software projects in distributed, collaborative and virtual environments, the software project management environment has changed. Traditional project management methods fail to address these added complexities. As a result, numerous software development projects either do not live up to expectations or they fail outright. The authors proposed the software project management model, entitled software project management supported by software agents (SPMSA) that aims to enhance software project management by taking the unique nature and changing environment of software projects into account. The SPMSA model supports the entire spectrum of software project management functionality, supporting and enhancing each key function with a team of software agents. In this extended paper, the authors adapt the SPMSA model to incorporate PMBOK2004 guidelines. The SPMSA model makes a fresh contribution to enhance software project management by utilising software agent technology.

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Recently, multisource domain adaptation (MDA) has begun to attract attention. Its performance should go beyond simply mixing all source domains together for knowledge transfer. In this article, we propose a novel prototype-based method for MDA. Specifically, for solving the problem that the target domain has no label, we use the prototype to transfer the semantic category information from source domains to target domain. First, a feature extraction network is applied to both source and target domains to obtain the extracted features from which the domain-invariant features and domain-specific features will be disentangled. Then, based on these two kinds of features, the named inherent class prototypes and domain prototypes are estimated, respectively. Then a prototype mapping to the extracted feature space is learned in the feature reconstruction process. Thus, the class prototypes for all source and target domains can be constructed in the extracted feature space based on the previous domain prototypes and inherent class prototypes. By forcing the extracted features are close to the corresponding class prototypes for all domains, the feature extraction network is progressively adjusted. In the end, the inherent class prototypes are used as a classifier in the target domain. Our contribution is that through the inherent class prototypes and domain prototypes, the semantic category information from source domains is transformed into the target domain by constructing the corresponding class prototypes. In our method, all source and target domains are aligned twice at the feature level for better domain-invariant features and more closer features to the class prototypes, respectively. Several experiments on public data sets also prove the effectiveness of our method.

Software projects have a high risk of cost and schedule overruns, which has been a source of concern for the software engineering community for a long time. One of the challenges in software project management is to make reliable prediction of delays in the context of constant and rapid changes inherent in software projects. This paper presents a novel approach to providing automated support for project managers and other decision makers in predicting whether a subset of software tasks (among the hundreds to thousands of ongoing tasks) in a software project have a risk of being delayed. Our approach makes use of not only features specific to individual software tasks (i.e. local data) -- as done in previous work -- but also their relationships (i.e. networked data). In addition, using collective classification, our approach can simultaneously predict the degree of delay for a group of related tasks. Our evaluation results show a significant improvement over traditional approaches which perform classification on each task independently: achieving 46% -- 97% precision (49% improved), 46% -- 97% recall (28% improved), 56% -- 75% F-measure (39% improved), and 78% -- 95% Area Under the ROC Curve (16% improved).

Collecting and preparing a large amount of data to train machine learning algorithms is time-consuming and not always feasible due to issues like sensor malfunctioning and privacy concerns. Transfer learning offers a promising solution to these problems. By leveraging prior knowledge gained from related tasks (i.e., source domain) it is possible to enhance the performance of a model on a new task (i.e., target domain) without extensive new data collection. However, the similarity between the source and target domains is crucial in transfer learning methods. Dissimilarity may result in a negative transfer, hampering the model's performance and hindering the energy analysis.Traditional clustering and similarity approaches have limitations in capturing a system's internal dynamics and temporal dependencies. Therefore, to get a better understanding of the underlying data-generation mechanism, it is crucial to uncover latent states that reveal hidden patterns and transitions not observable with conventional methods. For that purpose, this paper focuses on the optimal problem formulation for transfer learning in the context of building energy analysis. Specifically, the research steps include the implementation of an existing pattern-based Hidden Markov model (pHMM) to find pattern-based correlations between energy use time series collected in different buildings. The model is developed using a two-phase approach. In the first phase, the time series is segmented and clustered to provide a robust initial parameter estimate for the pHMM. Following this, the second phase involves iteratively refining the model by re-segmenting and re-clustering the time series based on the learned states. Finally, the correlation between the various domains is quantified by computing the similarities between the observed data patterns.In order to assess the effectiveness of the proposed approach, a same predictive model is evaluated across the given time series. The defined correlation metric is validated by examining whether the performance drops align with the indication provided. Additionally, commonly used correlation metrics, such as Dynamic Time Warping or Pearson Correlation Coefficient, are used as baselines. It is proven that the proposed pHMM can effectively identify and correlate patterns between time series, enhancing the accuracy of pattern detection and similarity assessment. Therefore, the presented method might significantly reduce data collection costs, while enabling accurate building energy analysis. In order to foster reproducibility and replicability of the results, the analysed energy data are retrieved from the Building Data Genome Project 2 dataset. Additionally, all codes developed during this study will be made available as an open-source repository.

This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.