Context: Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Objective: Although a myriad of research exists on software analytics as well as on Agile software development (ASD) practice on itself, there exists no systematic overview of the research done on ASD from a data analytics perspective. Therefore, the objective of this work is to make progress by linking ASD with Big Data analytics (BDA). Method: As the primary method to find relevant literature on the topic, we performed manual search and snowballing on papers published between 2011 and 2019. Results: In total, 88 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. Conclusions: As BDA and ASD are fast-developing areas, improving the productivity of software development teams is one of the most important objectives BDA is facing in the industry. This study provides scholars with information about the state of software analytics research and the current trends as well as applications in the business environment. Whereas, thanks to this literature review, practitioners should be able to understand better how to obtain actionable insights from their software artifacts and on which aspects of data analytics to focus when investing in such initiatives.

Big Data analytics in Agile software development: A systematic mapping study

The use of spreadsheets to capture information is widespread in industry. Spreadsheets can thus be a wealthy source of domain information. We propose to automatically extract this information and transform it into class diagrams. The resulting class diagram can be used by software engineers to understand, refine, or re-implement the spreadsheet’s functionality. To enable the transformation into class diagrams we create a library of common spreadsheet usage patterns. These patterns are localized in the spreadsheet using a two- dimensional parsing algorithm. The resulting parse tree is transformed and enriched with information from the library. We evaluate our approach on the spreadsheets from the Euses Spreadsheet Corpus by comparing a subset of the generated class diagrams with reference class diagrams created manually. Preprint accepted for publication in Proceedings of the 24th European Conference on Object-Oriented Programming (ECOOP 2010), Maribor (Slovenija), 21-25 June 2010, Lecture Notes in Computer Science, Springer-Verlag, 2010.

/pdf/automatically-extracting-class-diagrams-from-spreadsheets-ospetdcdwu.pdf

Automatically extracting class diagrams from spreadsheets

Contemporary software development strongly relies on software monitoring for different purposes, such as detecting abnormal behaviour or finding performance issues. These analyses are enabled by means of log data. The richness of log information has drawn the attention of researchers, who have put significant effort in software monitoring and log analysis techniques. Such knowledge, however, is currently spread. Moreover, we have no conceptual framework to explain the research field. In this paper, we perform a systematic literature review on logging techniques for software monitoring. More specifically, we explore the existing contemporary research on log engineering, infrastructure, and analysis. To that aim, we study 96 papers that appeared on top-level peer-reviewed conferences and journals. We then propose the Contemporary Logging Framework, a conceptual framework that maps the entire research field in four dimensions and 13 ramifications (i.e., research focuses). Finally, based on all the knowledge we gained, we propose a list of next steps that will help researchers in moving the field forward.

Contemporary Software Monitoring: A Systematic Literature Review.

Four years after the introduction of MOOCs – which were proclaimed to be «the end of education as we know it» in 2012 – the role and effect of these free, online courses is becoming clearer. The online means of delivery to the heterogeneous audiences of MOOCs have enabled and compelled instructors and course teams to develop innovative and flexible learning materials. We can analyse the data on the study behaviour of learners to identify which course elements are effective. In addition, the integration of elements of MOOCs in campus education has resulted in promising outcomes and positive reactions from both students and teachers. On the level of the institution, we also see the effect of MOOCs: ranging from new possibilities in communication and branding, to new needs for faculty development and the support organisation. Furthermore, MOOCs play a role in the unbundling of education, e.g. the learning experience and the assessment tasks now can be uncoupled and may be delivered by different institutions and by different means: the learning experience may be in the form of a MOOC and the assessment may be a written exam at an institution.

/pdf/moocs-and-their-effect-on-the-institution-experiences-in-1p0td1z12g.pdf

MOOCs and their Effect on the Institution: Experiences in Course Design, Delivery and Evaluation; Research; Faculty Development; Unbundling and Credits for MOOCs

Version information plays an important role in spreadsheet understanding, maintaining and quality improving. However, end users rarely use version control tools to document spreadsheets' version information. Thus, the spreadsheets' version information is missing, and different versions of a spreadsheet coexist as individual and similar spreadsheets. Existing approaches try to recover spreadsheet version information through clustering these similar spreadsheets based on spreadsheet filenames or related email conversation. However, the applicability and accuracy of existing clustering approaches are limited due to the necessary information (e.g., filenames and email conversation) is usually missing. We inspected the versioned spreadsheets in VEnron, which is extracted from the Enron Corporation. In VEnron, the different versions of a spreadsheet are clustered into an evolution group. We observed that the versioned spreadsheets in each evolution group exhibit certain common features (e.g., similar table headers and worksheet names). Based on this observation, we proposed an automatic clustering algorithm, SpreadCluster. SpreadCluster learns the criteria of features from the versioned spreadsheets in VEnron, and then automatically clusters spreadsheets with the similar features into the same evolution group. We applied SpreadCluster on all spreadsheets in the Enron corpus. The evaluation result shows that SpreadCluster could cluster spreadsheets with higher precision (78.5% vs. 59.8%) and recall rate (70.7% vs. 48.7%) than the filename-based approach used by VEnron. Based on the clustering result by SpreadCluster, we further created a new versioned spreadsheet corpus VEnron2, which is much bigger than VEnron (12,254 vs. 7,294 spreadsheets). We also applied SpreadCluster on the other two spreadsheet corpora FUSE and EUSES. The results show that SpreadCluster can cluster the versioned spreadsheets in these two corpora with high precision (91.0% and 79.8%).

SpreadCluster: recovering versioned spreadsheets through similarity-based clustering

Spreadsheets are popular end-user computing applicationsand one reason behind their popularity is that theyoffer a large degree of freedom to their users regarding theway they can structure their data. However, this flexibilityalso makes spreadsheets difficult to understand. Textual documentationcan address this issue, yet for supporting automaticgeneration of textual documentation, an important pre-requisiteis to extract metadata inside spreadsheets. It is a challengethough, to distinguish between data and metadata due to thelack of universally accepted structural patterns in spreadsheets. Two existing approaches for automatic extraction of spreadsheetmetadata were not evaluated on large datasets consisting ofuser inputs. Hence in this paper, we describe the collectionof a large number of user responses regarding identificationof spreadsheet metadata from participants of a MOOC. Wedescribe the use of this large dataset to understand how usersidentify metadata in spreadsheets, and to evaluate two existingapproaches of automatic metadata extraction from spreadsheets. The results provide us with directions to follow in order toimprove metadata extraction approaches, obtained from insightsabout user perception of metadata. We also understand what typeof spreadsheet patterns the existing approaches perform well andon what type poorly, and thus which problem areas to focus onin order to improve.

/pdf/evaluating-automatic-spreadsheet-metadata-extraction-on-a-16mp3vfly6.pdf

Evaluating Automatic Spreadsheet Metadata Extraction on a Large Set of Responses from MOOC Participants

Engineering modern large-scale software requires software developers to not solely focus on writing code, but also to continuously examine monitoring data to reason about the dynamic behavior of their systems. These additional monitoring responsibilities for developers have only emerged recently, in the light of DevOps culture. Interestingly, software development activities happen mainly in the IDE, while reasoning about production monitoring happens in separate monitoring tools. We propose an approach that integrates monitoring signals into the development environment and workflow. We conjecture that an IDE with such capability improves the performance of developers as time spent continuously context switching from development to monitoring would be eliminated. This paper takes a first step towards understanding the benefits of a possible monitoring-aware IDE. We implemented a prototype of a Monitoring-Aware IDE, connected to the monitoring systems of Adyen, a large-scale payment company that performs intense monitoring in their software systems. Given our results, we firmly believe that monitoring-aware IDEs can play an essential role in improving how developers perform monitoring.

/pdf/monitoring-aware-ides-4fkxc2k5bn.pdf

Monitoring-aware IDEs

It is important to detect problems fast and to have a clear overview of what is happening within a system after deployment to maximize the uptime and functional quality of the system. Therefore it is necessary to increase the awareness that developers have of errors and logs. Increasing the awareness that developers have of errors and logs has a positive impact on finding problems and solving them. This thesis aims to use monitoring information to bridge the gap that exists between development and operations. We propose to do this by linking the logs to source code to provide this missing link between development and operations. We provide a theory for a monitoring-aware IDE which aims to tackle some of the challenges and enhance some of the practices that exist in the field. We implemented a monitoring-aware IDE and performed a field study to measure its effect. Our results show that a monitoring-aware IDE assists the developer in understanding the system, fixing problems in the code, and improving the monitoring code.

/pdf/increasing-operational-awareness-using-monitoring-aware-ides-4goots9y6d.pdf

Jos Winter

Papers

Evaluating Automatic Spreadsheet Metadata Extraction on a Large Set of Responses from MOOC Participants

Monitoring-aware IDEs

Increasing Operational Awareness using Monitoring-Aware IDEs