11:00 - 11:30

• Enron's Spreadsheets and Related Emails: A Dataset and Analysis

  Spreadsheets are used extensively in business processes around the world and as such, are a topic of research interest. Over the past few years, many spreadsheet studies have been performed on the EUSES spreadsheet corpus. While this corpus has served the spreadsheet community well, the spreadsheets it contains are mainly gathered with search engines and might therefore not represent spreadsheets used in companies. This paper presents an analysis of a new dataset, extracted from the Enron email archive, containing over 15,000 spreadsheets used within the Enron Corporation. In addition to the spreadsheets, we also present an analysis of the associated emails, where we look into spreadsheet-specific email behavior. Our analysis shows that 1) 24% of Enron spreadsheets with at least one formula contain an Excel error, 2) there is little diversity in the functions used in spreadsheets: 76% of spreadsheets in the presented corpus use the same 15 functions and, 3) the spreadsheets are substantially more smelly than the EUSES corpus, especially in terms of long calculation chains. Regarding the emails, we observe that spreadsheets 1) are a frequent topic of email conversation with 10% of emails either referring to or sending spreadsheets and 2) the emails are frequently discussing errors in and updates to spreadsheets.

   

  Preprint available:

Felienne Hermans; Emerson Murphy-Hill

Delft University of Technology, Netherlands; North Carolina State University, USA

11:30 - 12:00

• An Empirical Study on Quality Issues of Production Big Data Platform

  Big Data computing platform has evolved to be a multi-tenant service. The service quality matters because system failure or performance slowdown could adversely affect business and user experience. There is few study in literature on service quality issues of production Big Data computing platform. In this paper, we present an empirical study on the service quality issues of Microsoft ProductA, which is a company-wide multi-tenant Big Data computing platform, serving thousands of customers from hundreds of teams. ProductA has a well-defined incident management process, which helps customers report and mitigate service quality issues on 24/7 basis. This paper explores the common symptom, causes and mitigation of service quality issues in Big Data computing. We conduct an empirical study on 210 real service quality issues in ProductA. Our major findings include (1) 21.0% of escalations are caused by hardware faults; (2) 36.2% are caused by system side defects; (3) 37.2% are due to customer side faults. We also studied the general diagnosis process and the commonly adopted mitigation solutions. Our findings can help improve current development and maintenance practice of Big Data computing platform, and motivate tool support.

   

  Preprint available:

Hucheng Zhou; Jian-Guang Lou; Hongyu Zhang; Haoxiang Lin; Tingting Qin

Microsoft Research, China

12:00 - 12:30

• Code Reviews Do Not Find Bugs, How the Current Code Review Best Practice Slows Us Down

  Because of its many uses and benefits, code reviews are a standard part of the modern software engineering workflow. Since they require involvement of people, code reviewing is often the longest part of the code integration activities. Using experience gained at Microsoft and with support of data, we posit (1) that code reviews often do not find functionality issues that should block a code submission, (2) that effective code reviews should be performed by people with specific set of skills, and (3) that the social aspect of code reviews cannot be ignored. We find that we need to be more sophisticated with our guidelines for the code review workflow. We show how our findings from code reviewing practice influence our code review tools at Microsoft. Finally, we assert that, due to its costs, code reviewing practice is a topic deserving to be better understood, systematized and applied to software engineering workflow with more precision than the best practice currently prescribes.

   

  Preprint available:

Jacek Czerwonka; Michaela Greiler; Jack Tilford

Microsoft Corporation, USA