Buy Perspectives on Data Science for Software Engineering by Thomas Zimmermann
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Home > Computing and Information Technology > Databases > Data mining > Perspectives on Data Science for Software Engineering
Perspectives on Data Science for Software Engineering

Perspectives on Data Science for Software Engineering


     0     
5
4
3
2
1



International Edition


X
About the Book

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

Table of Contents:
Introduction Perspectives on data science for software engineering Software analytics and its application in practice Seven principles of inductive software engineering: What we do is different The need for data analysis patterns (in software engineering) From software data to software theory: The path less traveled Why theory matters Success Stories/Applications Mining apps for anomalies Embrace dynamic artifacts Mobile app store analytics The naturalness of software Advances in release readiness How to tame your online services Measuring individual productivity Stack traces reveal attack surfaces Visual analytics for software engineering data Gameplay data plays nicer when divided into cohorts A success story in applying data science in practice There's never enough time to do all the testing you want The perils of energy mining: measure a bunch, compare just once Identifying fault-prone files in large industrial software systems A tailored suit: The big opportunity in personalizing issue tracking What counts is decisions, not numbers—Toward an analytics design sheet A large ecosystem study to understand the effect of programming languages on code quality Code reviews are not for finding defects—Even established tools need occasional evaluation Techniques Interviews Look for state transitions in temporal data Card-sorting: From text to themes Tools! Tools! We need tools! Evidence-based software engineering Which machine learning method do you need? Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds Parse that data! Practical tips for preparing your raw data for analysis Natural language processing is no free lunch Aggregating empirical evidence for more trustworthy decisions If it is software engineering, it is (probably) a Bayesian factor Becoming Goldilocks: Privacy and data sharing in “just right” conditions The wisdom of the crowds in predictive modeling for software engineering Combining quantitative and qualitative methods (when mining software data) A process for surviving survey design and sailing through survey deployment Wisdom Log it all? Why provenance matters Open from the beginning Reducing time to insight Five steps for success: How to deploy data science in your organizations How the release process impacts your software analytics Security cannot be measured Gotchas from mining bug reports Make visualization part of your analysis process Don't forget the developers! (and be careful with your assumptions) Limitations and context of research Actionable metrics are better metrics Replicated results are more trustworthy Diversity in software engineering research Once is not enough: Why we need replication Mere numbers aren't enough: A plea for visualization Don’t embarrass yourself: Beware of bias in your data Operational data are missing, incorrect, and decontextualized Data science revolution in process improvement and assessment? Correlation is not causation (or, when not to scream “Eureka!”) Software analytics for small software companies: More questions than answers Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions) What can go wrong in software engineering experiments? One size does not fit all While models are good, simple explanations are better The white-shirt effect: Learning from failed expectations Simpler questions can lead to better insights Continuously experiment to assess values early on Lies, damned lies, and analytics: Why big data needs thick data The world is your test suite

About the Author :
Tim Menzies, Full Professor, CS, NC State and a former software research chair at NASA. He has published 200+ publications, many in the area of software analytics. He is an editorial board member (1) IEEE Trans on SE; (2) Automated Software Engineering journal; (3) Empirical Software Engineering Journal. His research includes artificial intelligence, data mining and search-based software engineering. He is best known for his work on the PROMISE open source repository of data for reusable software engineering experiments. Laurie Williams, Full Professor and Associate Department Head CS, NC State. 180+ publications, many applying software analytics. She is on the editorial boards of IEEE Trans on SE; (2) Information and Software Technology; and (3) IEEE Software. is a researcher in the Research in Software Engineering (RiSE) group at Microsoft Research, adjunct assistant professor at the University of Calgary, and affiliate faculty at University of Washington. He is best known for his work on systematic mining of version archives and bug databases to conduct empirical studies and to build tools to support developers and managers. He received two ACM SIGSOFT Distinguished Paper Awards for his work published at the ICSE '07 and FSE '08 conferences.


Best Sellers


Product Details
  • ISBN-13: 9780128042069
  • Publisher: Elsevier Science & Technology
  • Publisher Imprint: Morgan Kaufmann Publishers In
  • Height: 235 mm
  • No of Pages: 408
  • Width: 191 mm
  • ISBN-10: 0128042060
  • Publisher Date: 12 Jul 2016
  • Binding: Paperback
  • Language: English
  • Weight: 910 gr


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Perspectives on Data Science for Software Engineering
Elsevier Science & Technology -
Perspectives on Data Science for Software Engineering
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Perspectives on Data Science for Software Engineering

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    Fresh on the Shelf


    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!