developer recommendation
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Author(s):  
Muhammad Zubair Baloch ◽  
Shahid Hussain ◽  
Humaira Afzal ◽  
Muhammad Rafiq Mufti ◽  
Bashir Ahmad

Author(s):  
Xinqiang Xie ◽  
Xiaochun Yang ◽  
Bin Wang ◽  
Qiang He

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1215 ◽  
Author(s):  
Varun Gupta ◽  
Jose Maria Fernandez-Crehuet ◽  
Thomas Hanne

[Context] Freelancers could catalyze the software development process by providing their niche skills to generate high quality outputs. They could help companies (including startups) to foster innovations by suggesting creative ideas and providing their expertise in implementing them (for instance, designing solutions, coding solutions etc.). Freelancers could effectively and efficiently work as a virtual member of the software development team. The company must make informed decisions about which task to allot to the freelancer, which freelancer to select, pricing the task, and evaluating the submitted work. On the other hand, the freelancer should make an informed decision about evaluating the monetary value of the task to be charged, trusting the requester, analyzing the skills requirement of the task (finding matches between skill requirement and skills processed), selecting the best task, and maintaining the highest level of reputation. However, the literature does not provide freelancers and the companies the guidelines that support their decision making. However, if freelancers are selected carefully for the most suitable task, the companies will benefit a lot in terms of improved software development metrics. [Objectives] The objective of this paper is to provide the research community the research trends in freelancer-supported software development. This helps to understand that which software development areas have higher concentrations of research efforts, which area has the support of empirical evidence to support management decision makings, and which area requires the research attention. [Method] The systematic study is conducted by planning the mapping protocol, executing the protocol, and reporting the findings using various visualization tools like bar charts and pie charts. The search process was planned to be executed using set of inclusion and exclusion conditions on four bibliographic databases (IEEExplore, Springerlink, Sciencedirect, and ACM digital library). The relevant papers are selected by applying inclusion and exclusion conditions. The google citations of the relevant papers are subject to the inclusion and exclusion conditions again to include the more relevant papers. Finally, the systematic schema was created and populated after analyzing the studies abstracts. [Results] The results indicate the following (a) The research focus is on generic software development (78%) rather on individual life cycle activities. (b) The number of empirical studies is limited (25%). (c) A number of studies proposing solutions and evaluating on live cases in industrial settings are missing from the literature. This is in comparison to the validation approaches (72%) i.e., solutions tested in laboratory settings. (d) At present, the literature has limited ability to provide the software companies (including startups) with the guidelines (in the form of opinions and experience reports) for involving freelancers in the software development process. (e) The reported challenges include Collaboration and Coordination (33%), Developer Recommendation (or selection) (19%), Team Formulation (14%), Task Recommendation (allocation) (14%), Task Decomposition (11%), Privacy and Security (Confidentiality) (11%), Budget Estimation (8%), Recognition (8%), Trust Issues (8%), Market Dynamism (6%), Intellectual Property Issues (6%), Participation of Crowd Worker (6%), and Capacity Utilization (3%). These challenges are highly interactive, and each challenge impacts all other challenges. (e) Recent focus of the researchers (total 7 studies in 2019) is on generic software development handling the collaboration and coordination (3 studies out of 7), Developer recommendation (2 studies out of 7), and task recommendation (2 studies out of 7). [Conclusion] The freelancer-driven software engineering research area has got the attraction of the researchers, but it will take a long time to gain maturity. This puts an urgent call for more empirical studies and evaluation-based solution research that could help companies (including startups) to foster innovations. Further, the research focus should be well distributed among the various development phases to address the unique challenges associated with individual activities. The accurate management of the freelancer in the software development could help companies and startups to foster innovations and remain competitive in the marketplace.


Author(s):  
Matheus Camilo da Silva ◽  
Andre Armstrong Janino Cizotto ◽  
Emerson Cabrera Paraiso

2020 ◽  
Vol 10 (12) ◽  
pp. 4333 ◽  
Author(s):  
Xinqiang Xie ◽  
Bin Wang ◽  
Xiaochun Yang

Collaboration efficiency is of primary importance in software development. It is widely recognized that choosing suitable developers is an efficient and effective practice for improving the efficiency of software development and collaboration. Recommending suitable developers is complex and time-consuming due to the difficulty of learning developers’ expertise and willingness. Existing works focus on learning developers’ expertise and interactions from their explicit historical information and matching them to specific task. However, such procedures may suffer low accuracy because they ignore implicit information, such as (1) developer–developer collaboration relationships, (2) developer–task implicit interaction relationships, and (3) task–task association relationships, etc. To that end, this paper proposes a multi-relationship fused approach for software developer recommendation (termed SoftRec). First, in addition to explicit developer–task interactions, it considers multivariate implicit relationships, including the three types mentioned above. Second, it integrates these relationships based on joint matrix factorization and generates forecast results upon the architecture of deep neural network. Furthermore, we propose a fast update method to address the cold start issue by making online recommendations for new developers and new tasks. Extensive experiments are conducted on two real-world datasets, and a user study is conducted in a well-known software company. The results demonstrate that SoftRec outperforms four state-of-the-art works.


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