Multiple Software Projects Research Articles

A SURVEY OF THE PROBLEM AND TECHNIQUES IN RESOURCE ALLOCATION FOR MULTI-PROJECT SOFTWARE PROJECTS

Managing multiple software projects concurrently presents challenges in resource allocation. This study explores these issues and potential solutions. We conducted research to identify and categorise the most pressing problems in this domain and evaluate the effectiveness of various techniques proposed to mitigate these challenges. The key challenges identified are resource underutilisation, inefficiency, and conflict. Projects often suffer from long durations, uncertainties, and cost overruns. Traditional project management methods, such as the Critical Path Method and Program Evaluation and Review Technique, assume unlimited resources and are designed for single-project environments, rendering them inadequate for multi-project scenarios. Consequently, more recent research has explored advanced approaches to develop more effective scheduling solutions, including heuristic algorithms, genetic algorithms, and hybrid methods. Still, they lack performance guarantees and may not consistently achieve optimal results. The study examines how researchers tackle these issues. Techniques include using upper and lower bounds for concurrent activities, project buffers, and optimising project makespan for better resource utilisation. Additionally, allocating the right people to projects and coordinated planning for resource-constrained scenarios are explored. The research indicates that multi-stage hybrid approaches utilising machine learning techniques yield the most promising results. Therefore, future research should focus on applying ML techniques to select the optimal algorithm and incorporate historical data, representing a promising direction for advancing this field. This analysis provides insights for researchers seeking to develop improved approaches to resource allocation in multi-project software development.

نموذج مقترح لإدارة المخاطر في الشركات ذات المشروعات المتعددة

Recently, the importance of risk management has increased in the field of project management, especially software projects. The importance of risk management is because it has direct effects on the success of the project. Therefore, software organizations face a major challenge in achieving effective and efficient risk management. Therefore, measuring risks in software development is a key activity to be undertaken in risk management. Risk measurement is also one of the important topics that need more effort, especially in a work environment with multiple software projects. A multi-project environment involves multiple projects sharing the same available resources. This research aims to conduct a literature review to discover patterns or trends in software development to measure project risks and to reveal aspects of the measurement process, risk indicators, and various metrics for software projects. This research also aims to present a proposed framework for assessing risks in a multi-project work environment with shared resources for different levels in organizations with different capabilities.

Enhance Risks Management of Software Development Projects in Concurrent Multi-Projects Environment to Optimize Resources Allocation Decisions

In software development project management, Risk management represents critical knowledge and skills at the level of a single software project and at the enterprise ‎level, which executes multiple software projects concurrently. ‎The best decision of Risk management contributes to optimizing resource allocation ‎ at the enterprise level for achieving its goals. Therefore, the issue needs centralized risk management at the enterprise level as a whole and not for each project. Risk management is implemented through several stages and using different methods. ‎Various studies deal with multiple aspects of software ‎management. This research provides an analytical view of risk assessment in multi-environment software development projects that take place simultaneously. The study uses a public dataset previously used in previous research for several simultaneous projects in one organization. It describes the multi-software project's risks through 12 variables. A comparative ‎ analysis uses classification methods (‎Random Forest- TreesJ48 - REP Tree - Simple Logistic)‎ to assess risks and put them in central view. The research experiment has proven high accuracy in determining risk levels in a multi-project environment, reaching approximately 98%, using the REP tree technique.

AutoToolVCC: Variable consistency check tool for automotive software projects

AbstractAutomotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s.

Learning actionable analytics from multiple software projects

The current generation of software analytics tools are mostly prediction algorithms (e.g. support vector machines, naive bayes, logistic regression, etc). While prediction is useful, after prediction comes planning about what actions to take in order to improve quality. This research seeks methods that generate demonstrably useful guidance on “what to do” within the context of a specific software project. Specifically, we propose XTREE (for within-project planning) and BELLTREE (for cross-project planning) to generating plans that can improve software quality. Each such plan has the property that, if followed, it reduces the expected number of future defect reports. To find this expected number, planning was first applied to data from release x. Next, we looked for change in release x + 1 that conformed to our plans. This procedure was applied using a range of planners from the literature, as well as XTREE. In 10 open-source JAVA systems, several hundreds of defects were reduced in sections of the code that conformed to XTREE’s plans. Further, when compared to other planners, XTREE’s plans were found to be easier to implement (since they were shorter) and more effective at reducing the expected number of defects.

Workflow temporal verification for monitoring parallel business processes

AbstractWorkflow temporal verification is conducted to guarantee on‐time completion, which is one of the most important QoS (Quality of Service) dimensions for business processes running in the cloud. However, as today's business systems often need to handle a large number of concurrent customer requests, conventional response‐time based process monitoring strategies conducted in a one‐by‐one fashion cannot be applied efficiently to a large batch of parallel processes because of significant time overhead. Similar situations may also exist in software companies where multiple software projects are carried out at the same time by software developers. To address such a problem, based on a novel runtime throughput consistency model, this paper proposes a QoS‐aware throughput based checkpoint selection strategy, which can dynamically select a small number of checkpoints along the system timeline to facilitate the temporal verification of throughput constraints and achieve the target on‐time completion rate. Experimental results demonstrate that our strategy can achieve the best efficiency and effectiveness compared with the state‐of‐the‐art as and other representative response‐time based checkpoint selection strategies. Copyright © 2016 John Wiley & Sons, Ltd.

How the Apache community upgrades dependencies: an evolutionary study

Software ecosystems consist of multiple software projects, often interrelated by means of dependency relations. When one project undergoes changes, other projects may decide to upgrade their dependency. For example, a project could use a new version of a component from another project because the latter has been enhanced or subject to some bug-fixing activities. In this paper we study the evolution of dependencies between projects in the Java subset of the Apache ecosystem, consisting of 147 projects, for a period of 14 years, resulting in 1,964 releases. Specifically, we investigate (i) how dependencies between projects evolve over time when the ecosystem grows, (ii) what are the product and process factors that can likely trigger dependency upgrades, (iii) how developers discuss the needs and risks of such upgrades, and (iv) what is the likely impact of upgrades on client projects. The study results--qualitatively confirmed by observations made by analyzing the developers' discussion--indicate that when a new release of a project is issued, it triggers an upgrade when the new release includes major changes (e.g., new features/services) as well as large amount of bug fixes. Instead, developers are reluctant to perform an upgrade when some APIs are removed. The impact of upgrades is generally low, unless it is related to frameworks/libraries used in crosscutting concerns. Results of this study can support the understanding of the of library/component upgrade phenomenon, and provide the basis for a new family of recommenders aimed at supporting developers in the complex (and risky) activity of managing library/component upgrade within their software projects.

Applicability of Inter Project Validation for Determination of Change Prone Classes

research in the field of defect and change proneness prediction of software has gained a lot of momentum over the past few years. Indeed, effective prediction models can help software practitioners in detecting the change prone modules of a software, allowing them to optimize the resources used for software testing. However, the development of the prediction models used to determine change prone classes are dependent on the availability of historical data from the concerned software. This can pose a challenge in the development of effective change prediction models. The aim of this paper is to address this limitation by using the data from models based on similar projects to predict the change prone classes of the concerned software. This inter project technique can facilitate the development of generalized models which can be used to ascertain change prone classes for multiple software projects. It would also lead to optimization of critical time and resources in the testing and maintenance phases. This work evaluates the effectiveness of statistical and machine learning techniques for developing such models using receiver operating characteristic analysis. The observations of the study indicate varied results for the different techniques used.

An Automated Approach for Job Scheduling and Work Flow Mining

Now a day’s work allotment in a software firm become more important and cumbersome. The main objective of this concept is to reduce the work of the software developers in a software company and work allocation. Here the work will be allotted for the each person automatically by splitting the modules into tasks. When developing a big project more confusion and more discussions will occur, and some clauses will arise between modules while splitting into tasks and who the person to develop the particular task And performance evolution will not be in accurate, which confuse with the actual work allotment by the HR. So that still companies are struggling to book multiple software projects at a same time. So that still companies are yielding less works from their employees. In order to reduce these problems, implementing work flow mining, some of them are Group movement pattern, Heterogeneous project details, Ranking model adaptation SVM (RA-SVM), Domain specific search, Distributed clustering etc.

Evolutionary Optimization of Software Quality Modeling with Multiple Repositories

A novel search-based approach to software quality modeling with multiple software project repositories is presented. Training a software quality model with only one software measurement and defect data set may not effectively encapsulate quality trends of the development organization. The inclusion of additional software projects during the training process can provide a cross-project perspective on software quality modeling and prediction. The genetic-programming-based approach includes three strategies for modeling with multiple software projects: Baseline Classifier, Validation Classifier, and Validation-and-Voting Classifier. The latter is shown to provide better generalization and more robust software quality models. This is based on a case study of software metrics and defect data from seven real-world systems. A second case study considers 17 different (nonevolutionary) machine learners for modeling with multiple software data sets. Both case studies use a similar majority-voting approach for predicting fault-proneness class of program modules. It is shown that the total cost of misclassification of the search-based software quality models is consistently lower than those of the non-search-based models. This study provides clear guidance to practitioners interested in exploiting their organization's software measurement data repositories for improved software quality modeling.

Using the DEMO methodology for modeling open source software development processes

Context Open source software development (OSSD) process modeling has received increasing interest in recent years. These efforts aim to identify common elements in the development process between multiple open source software (OSS) projects. However, the complexity inherent to OSSD process modeling puts significant demands on the modeling language. Objective In this paper, we propose that the Design and Engineering Methodology for Organizations (DEMO) may provide an interesting alternative to develop OSSD process models. DEMO exhibits two unique features within the context of OSSD process modeling. First, DEMO analyzes processes at the ontological level and provides high-level process descriptions, instead of focusing on the implementation level. Second, DEMO studies the communication patterns between human actors, instead of the sequences in which activities are performed. Method We investigate the feasibility of using DEMO to construct OSSD process models by means of a case study. DEMO models were constructed to describe the NetBeans Requirements and Release process. In addition, the quality of these DEMO models was evaluated using a quality framework for conceptual modeling. Results Our results showed that our DEMO models exhibited a high level of abstraction, thereby reducing the complexity of the OSSD process models. In addition, the evaluation of the models developed in this paper by using the quality framework for conceptual modeling showed that the models were of high quality. Conclusions We have shown that the DEMO methodology can be successfully used to model OSSD processes and to obtain abstract and high-quality OSSD process models. However, given some potential drawbacks with respect to understandability and implementability, we primarily propose the use of DEMO within OSSD process modeling as an analysis tool that should be complemented with other techniques and models for communication and reenactment purposes.

Discovering Software Reliability Patterns Based On Multiple Software Projects

Discovering patterns that indicate software reliability provides valuable information to software project managers. Software Quality Classification (SQC) modeling is a methodology that can be used to discover reliability patterns of large software projects. However, the patterns found by SQC modeling may not be accurate and robust owing to insufficient information used in the training process. This study compares two genetic programming-based SQC models using different volumes of data. These data were extracted from seven different NASA software projects. The results demonstrate that combining data from different projects can produce more accurate and reliable patterns.

Robust Preemptive Resource Assignment for Multiple Software Projects Using Parameter Design

Project preemption allows resources flow passively from one project or task to another. Due to high-cost and high-risk characteristics of software development, project preemption might have a significant impact on the ultimate completion time of a software project. This arti- cle examines the feasibility of the Taguchi's parameter design to achieve robust resource as- signment for multiple software projects with different preemptive rules. Two examples of soft- ware projects is employed to demonstrate the design process of the parameter design applying in the project scheduling problems. The first project illustrates a preemptive single software project. Project preemption occurs when resource vacations are considered. The second example consid- ers problems of preemptive multiple project scheduling. The parameter design with preemption rules and scheduling heuristics are employed to solve the problem. The results show that the pa- rameter design can achieve robust resource assignment by minimizing the effect of task varia- tion.

Managing embedded software project team knowledge

In modern large new product development (NPD) organisations the embedded software teams do not work in isolation but develop software in complex interrelationships with the other teams of the product development. Specifically the software team must interface with the system and hardware developments. Ambitious industrial product development programs require typically concurrent engineering of the various components of the system product, making it even more complicated for the software development to capture all the relevant information on time. Furthermore, multiple software projects may be working concurrently on different release versions of the same product family and platform core assets. In such a cross-functional environment, building and managing the embedded software team body of knowledge is not trivial. The paper proposes methods for managing such knowledge with a systematic modelling approach. The methods help in capturing project-specific context-driven key information. The empirical background of the study is in certain business units of Nokia Networks developing embedded software for telecommunications equipment products.