Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

BANKING SOFTWARE UPGRADE - GENERATING THE GDPR DECLARATION

The purpose of the paper is to present an approach for generating a GDPR declaration from banking software. Modern information technologies are used. The paper presents an approach to upgrading banking software so that the GDPR declaration can be printed quickly and easily, the GDPR declaration form of the banking specialists can be corrected, and it can be monitored which clients have signed the declaration. The proposal can be adapted by any banking software developer.

Comprehensive Study of MERN Stack - Architecture, Popularity and Future Scope

The biome of JavaScript is constantly evolving and a new framework or library is launched on a regular basis claiming better features. This study focuses on giving guideline to the reader in the process of choosing the best technology by comparing the runtime performance of the MERN (MongoDB, Express, React.js and Node.js) and MEVN (V stands for Vue.js) stack as well as increasing their workability in the job market. To fulfill the purpose, an experiment was conducted to conclude how swift the said stacks perform in building a single page application. For the experiment, two easy to-do applications are built with MERN and MEVN and loading time, adding time, updating and deleting time of the tasks are measured. Moreover, to be able to reveal the software stack trend among the Swedish-based companies, a survey study was conducted. Out of approximately 70 companies contacted, 12 responded. Due to the low number of response on the survey drawing conclusions from the survey and generalizing, the result was challenging. However, the results gathered show that all the respondents use either Vue.js or React.js or both as their client-side software though they are not always combined with Node.js and MongoDB. Other preferred server-side software that are used in combination with React.js or Vue.js are Java, Go and Django. Some of the main factors that the respondents pointed out that affects their choice of software was the ease of learning, the community behind the software, clients’ need and availability of that specific software developer.

A Conversation with Margo Seltzer and Mike Olson

Kirk McKusick sat down with Margo Seltzer and Mike Olson to discuss the history of Berkeley DB, for which they won the ACM Software System Award in 2021. Kirk McKusick has spent his career as a BSD and FreeBSD developer. Margo Seltzer has spent her career as a professor of computer science and as an entrepreneur of database software companies. Mike Olson started his career as a software developer and later started and managed several open-source software companies. Berkeley DB is a production-quality, scalable, NoSQL, Open Source platform for embedded transactional data management.

Apps as a tool against food loss and waste. Technical implications and implementation complications

The study of environmental crisis that affects the world in contemporary times involves the analysis of different factors which contribute to a bigger problem. One of these factors is food loss and waste. This issue originates as well from different factors that can be approached from different strategies. This article analyses the use of smartphone apps as a tool to fight food loss and waste. Theoretical bases that support the use of these tools as a viable option to mitigate environmental problems and the different implications of food loss and waste. It is then analyzed the technical requirements of apps and considered their economic viability from the software developer and entrepreneur respectively. Finally, it is exemplified with a case study of a failed anti-food waste app launch in Guadalajara. Results show that these apps design is a development that requires of many resources compared to a slow return on investment that will hardly ensure the app sustainability. Potential reaches of the app are high, but hard to accomplish without a solid structure behind and, ideally, support from government or private institutions that are more interested in environmental impact than financial profit.

Supplies and Equipment Inventory, Monitoring and Tracking Management System using Data Mining Techniques

In the present time, there are lot of web and software developer who provides different types of databased and online system to ease the burden of the different supply officer’s/inventory officers of different companies and government sectors but Cagayan State University is one of the big universities that remained inventory management in a manual way. This study together with the development of SEIMTMS was conducted to innovate the current system used and to abolish the difficulties and challenges encountered by the Supply Office staffs in inventory management, record keeping, monitoring and tracking, and report generation. Classification and clustering techniques were utilized to produce information and comprehensive decision support reports that aids the Supply Officer and University administration on decision- making and budget allocation. Furthermore, the system used Clustering technique together with MFP algorithm to forecast the frequently purchased supplies and frequently repaired equipment. These decision support reports are essential for Office Heads in identifying items to be purchased for a particular quarter. With the use of ISO/IEC 25010:2011 Software Quality Standards, the system was evaluated by IT Experts with a mean 4.67, qualitatively described as “very graet extent”.

The 2021 software developer shortage is coming

Companies must address the difficulty of hiring and retaining high-skilled employees from an increasingly smaller labor supply.

The Influence of Code Retrieval from the Web on Programmer’s Skills, Meth-odologies, and Coding Behaviors

The development of software projects consists of several stages, such as analysis and design. It also requires a set of skills that the software developer can use to work on the project, such as specifying the requirements and writing code. Developers usually search for source code on the internet for remix and reuse in software production. This paper aims to investigate the influence and effect of code retrieved from the web on programmers’ views, decisions, and skills. A questionnaire instrument was designed and distributed to programmers for their feedback. As a result, we were able to address some points and achieved a better understanding of the interaction between programmers and the code from the web, especially the code from programming forums such as Stack Over Flow.

On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models

Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.