Software analytics: Difference between revisions

Revision as of 04:44, 24 August 2012

Latest revision as of 04:35, 13 August 2025

(108 intermediate revisions by 60 users not shown)

Line 1:

{{Expert needed|software|reason=it appears to misrepresent the history of software analytics|date=December 2014}}

'''Software analytics''' is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services.

'''Software analytics''' is the [[analytics]] specific to the ___domain of [[software system]]s taking into account [[source code]], static and dynamic characteristics (e.g., [[software metric]]s) as well as related processes of their [[software development|development]] and [[software evolution|evolution]]. It aims at describing, monitoring, predicting, and improving the efficiency and effectiveness of [[software engineering]] throughout the [[software lifecycle]], in particular during [[software development]] and [[software maintenance]]. The data collection is typically done by mining [[software repository|software repositories]], but can also be achieved by collecting user actions or production data.

Insightful information is information that conveys meaningful

and useful understanding or knowledge towards performing the target

task. Typically insightful information is not easily attainable by

directly investigating the raw data without aid of analytic technologies.

Actionable information is information upon which software

practitioners can come up with concrete solutions (better than existing

solutions if any) towards completing the target task.

== Definitions ==

A huge wealth of various data exists in software lifecycle, including source code, feature specifications, bug reports, test cases, execution traces/logs, and real-world user feedback, etc. Data plays an essential role in modern software development, because hidden in the data is information about the quality of software and services as well as the dynamics of software development. With various analytical and computing technologies, such as pattern recognition, machine learning, data mining, information visualization and large-scale data computing & processing, software analytics is to enable software practitioners to perform effective and efficient data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks in engineering software and services.

* "Software analytics aims to obtain insightful and actionable information from software artifacts that help practitioners accomplish tasks related to software development, systems, and users."<ref name="IEEESoftware-2013" /> --- centers on analytics applied to artifacts a software system is composed of.

Software analytics broadly focus on trinity of software systems, software users, and software development process:

* "Software analytics is analytics on software data for managers and software engineers with the aim of empowering software development individuals and teams to gain and share insight form their data to make better decisions."<ref name="ICSESEIP12InfoNeeds"/> --- strengthens the core objectives for methods and techniques of software analytics, focusing on both software artifacts and activities of involved developers and teams.

* "Software analytics (SA) represents a branch of [[big data]] analytics. SA is concerned with the analysis of all software artifacts, not only source code. [...] These tiers vary from the higher level of the management board and setting the enterprise vision and portfolio management, going through project management planning and implementation by software developers."<ref name="BIGDSE2015" /> --- reflects the broad scope including various stakeholders.

== Aims ==

'''Software Systems'''. Depending on scale and complexity, the spectrum of software systems can span from operating systems for devices to large networked systems that consist of thousands of servers. System quality such as reliability, performance and security, is the key to success of modern software systems. As the system scale and complexity greatly increase, larger amount of data, e.g., run-time traces and logs, is generated; and data has become a critical media to monitor, analyze, understand and improve system quality.

Software analytics aims at supporting decisions and generating insights, i.e., findings, conclusions, and evaluations about software systems and their implementation, composition, behavior, quality, evolution as well as about the activities of various stakeholders of these processes.

* Insightful information obtained by software analytics conveys meaningful and useful understanding or knowledge towards performing target tasks. Typically, it cannot be easily obtained by direct examining raw big data without the aid of analytics methods and techniques.

* Actionable information obtained by software analytics steers or prescribes solutions that stakeholders in software engineering processes may take (e.g., software practitioners, development leaders, or C-level management).

== Approach ==

'''Software Users'''. Users are (almost) always right because ultimately they pay for the software and services in various ways. Therefore, it is important to continuously create the best user experience. Usage data collected from the real world reveals how users interact with software and services. The data is incredibly valuable for software practitioners to better understand their customers and gain insights on how to improve user experience accordingly.

Methods, techniques, and tools of software analytics typically rely on gathering, measuring, analyzing, and visualizing information found in the manifold data sources stored in software development environments and ecosystems. Software systems are well suited for applying analytics because, on the one hand, mostly formalized and precise data is available and, on the other hand, software systems are extremely difficult to manage ---in a nutshell: "software projects are highly measurable, but often unpredictable."<ref name="ICSESEIP12InfoNeeds"/>

'''Software Development Process'''. Software development has evolved from its traditional form to exhibit different characteristics. The process is more agile and engineers are more collaborative. Analytics on software development data provides a powerful mechanism that we can leverage in order to achieve higher development productivity.

Core data sources include [[source code]], "check-ins, work items, bug reports and test executions [...] recorded in software repositories such as CVS, Subversion, GIT, and Bugzilla."<ref>Harald Gall, Tim Menzies, [[Laurie Williams (software engineer)|Laurie Williams]], and Thomas Zimmerman. "Software Development Analytics". Dagstuhl Reports, Vol. 4, Issue 6, pp. 64-83.</ref> [[telemetry | Telemetry data]] as well as execution traces or logs can also be taken into account.

Automated analysis, massive data, and systematic reasoning support decision-making at almost all levels. In general, key technologies employed by software analytics include analytical technologies such as [[machine learning]], [[data mining]], [[statistics]], [[pattern recognition]], [[information visualization]] as well as large-scale data computing & processing. For example, software analytics tools allow users to map derived analysis results by means of [[software map]]s, which support interactively exploring system artifacts and correlated software metrics. There are also software analytics tools using analytical technologies on top of [[software quality]] models in [[agile software development]] companies, which support assessing software qualities (e.g., reliability), and deriving actions for their improvement.<ref>{{Cite journal|last1=Martínez-Fernández|first1=Silverio|last2=Vollmer|first2=Anna Maria|last3=Jedlitschka|first3=Andreas|last4=Franch|first4=Xavier|last5=Lopez|first5=Lidia|last6=Ram|first6=Prabhat|last7=Rodriguez|first7=Pilar|last8=Aaramaa|first8=Sanja|last9=Bagnato|first9=Alessandra|date=2019|title=Continuously assessing and improving software quality with software analytics tools: a case study|journal=IEEE Access|volume=7|pages=68219–68239|doi=10.1109/ACCESS.2019.2917403|issn=2169-3536|url=https://upcommons.upc.edu/bitstream/2117/133374/1/FINAL-Access-Paper-preprint.pdf|doi-access=free|bibcode=2019IEEEA...768219M }}</ref>

== History ==

{{Expert needed|Software|reason=it misrepresents the history of software analytics, strengthening a single researcher group that claims to have coined the expression software analytics|date=August 2017}}

In May 2009, Software Analytics was first proposed when [http://research.microsoft.com/en-us/groups/sa/ the Software Analytics Group (SA) at Microsoft Research Asia (MSRA)] was founded. The term becomes well known in the software engineering research community after a series of tutorials on software analytics were given at software engineering conferences such as IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), IEEE-CS Conference on Software Engineering Education and Training ([http://conferences.computer.org/cseet/ CSEE&T 2012]), International Conference on Software Engineering ([http://www.ifi.uzh.ch/icse2012/ ICSE 2012]) - Software Engineering in Practice Track, and a keynote talk given by Dr. Dongmei Zhang (the manager of the SA Group at MSRA) at the 9th Working Conference on Mining Software Repositories ([http://2012.msrconf.org/ MSR 2012]).

In 2009, the term "software analytics" was used in a paper by [[Dongmei Zhang]], Shi Han, Yingnong Dang, Jian-Guang Lou, and Haidong Zhang in part by the Software Analytics Group (SA) at [[Microsoft Research]] Asia (MSRA).<ref>{{Cite web |last=Brannon |first=Brian G. |date=23 June 2013 |title=Software Analytics in Practice |url=https://www.microsoft.com/en-us/research/wp-content/uploads/2016/07/ieeesoft13-softanalytics.pdf |access-date=31 December 2024 |website=microsoft.com}}</ref>

In November 2010, Software Development Analytics (Software Analytics with focus on Software Development) was first proposed by [http://research.microsoft.com/en-us/groups/ese/ the Empirical Software Engineering Group (ESE) at Microsoft Research Redmond] in their FoSER 2010 paper. A goldfish bowl panel on software development analytics was organized at the 34th International Conference on Software Engineering ([http://www.ifi.uzh.ch/icse2012/ ICSE 2012]), Software Engineering in Practice track.

The term has since become well known in the [[software engineering]] research community after a series of tutorials and talks on software analytics were given by the Software Analytics Group, in collaboration with Tao Xie from [[North Carolina State University]], at software engineering conferences including a tutorial at the IEEE/ACM [[International Conference on Automated Software Engineering]] (ASE 2011),<ref name="ASE2011" /> a talk at the International Workshop on Machine Learning Technologies in Software Engineering (MALETS 2011),<ref name="MALETS" /> a tutorial and a keynote talk given by Zhang at the IEEE-CS Conference on Software Engineering Education and Training,<ref name="CSEETKeynote" /><ref name="CSEETTutorial" /> a tutorial at the International Conference on Software Engineering - Software Engineering in Practice Track,<ref name="ICSETutorial" /> and a keynote talk given by Zhang at the Working Conference on Mining Software Repositories.<ref name="MSRKeynote" />

In November 2010, Software Development Analytics (Software Analytics with a focus on Software Development) was first proposed by [http://research.microsoft.com/en-us/groups/ese/Thomas Zimmermann and his colleagues at the Empirical Software Engineering Group (ESE) at Microsoft Research Redmond] in their FoSER 2010 paper.<ref name="FoSER" /> A goldfish bowl panel on software development analytics was organized by Zimmermann and [[Tim Menzies]] from West Virginia University at the 34th International Conference on Software Engineering ([http://www.ifi.uzh.ch/icse2012/ ICSE 2012]), Software Engineering in Practice trackTrack.<ref name="Goldfish" />

==See also==

* [[Mining Software Repositories]]

* [[Software maintenance]]

* [[Software archaeology]]

* [[Software development]]

* [[Software development process]]

* [[User experience]]

* [[Computer software]]

* [[Application software]]

* [[Software industry]]

* [[Analytics]]

== References ==

{{Reflist|

* Raymond P. L. Buse and Thomas Zimmermann. "Analytics for Software Development." In Proceedings of the Workshop on Future of Software Engineering Research (FoSER 2010) Santa Fe, NM, USA, November 2010, pp. 77-80. [http://research.microsoft.com/apps/pubs/default.aspx?id=136301 PDF]

refs=

* Dongmei Zhang, Yingnong Dang, Jian-Guang Lou, Shi Han, Haidong Zhang, and Tao Xie. "Software Analytics as a Learning Case in Practice: Approaches and Experiences". In Proceedings of International Workshop on Machine Learning Technologies in Software Engineering (MALETS 2011), Lawrence, Kansas, November 2011. [http://people.engr.ncsu.edu/txie/publications/malets11-analytics.pdf PDF][https://sites.google.com/site/xsoftanalytics/malets11-msrasa.pdf?attredirects=0 Slides]

*<ref Dongmeiname="FoSER">Raymond P. L. ZhangBuse and TaoThomas XieZimmermann. "xSA: eXtreme Software Analytics - Marriage of eXtreme Computing andfor Software AnalyticsDevelopment." In Proceedings of the 26thWorkshop IEEE/ACMon InternationalFuture Conference on Automatedof Software Engineering Research (ASEFoSER 20112010), TutorialSanta Fe, LawrenceNM, KansasUSA, November 20112010, pp. 77-80.</ref>[http://research.microsoft.com/apps/pubs/default.aspx?id=136301 PDF]

* Dongmei Zhang, Yingnong Dang, Shi Han, and Tao Xie. "Teaching and Training for Software Analytics." In Proceedings of the 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T 2012), Tutorial, Nanjing, China, April 2012.

*<ref name="MALETS">Dongmei Zhang, Yingnong Dang, Jian-Guang Lou, Shi Han, Haidong Zhang, and Tao Xie. "Software Analytics as a Learning Case in Practice: MiniApproaches Tutorial.and Experiences". In Proceedings of the 34th International ConferenceWorkshop on SoftwareMachine EngineeringLearning (ICSETechnologies 2012),in Software Engineering in(MALETS Practice2011), Mini TutorialLawrence, ZurichKansas, Switzerland, June 2012,November pp. 9972011. [http://researchpeople.microsoftengr.comncsu.edu/entxie/publications/malets11-usanalytics.pdf PDF][https:/groups/sasites.google.com/softwareanalyticsinpractice_minitutorial_icse2012site/xsoftanalytics/malets11-msrasa.pdf?attredirects=0 Slides]</ref>

* Tim Menzies and Thomas Zimmermann. "Goldfish Bowl Panel: Software Development Analytics." In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Software Engineering in Practice, Zurich, Switzerland, June 2012, pp. 1032-1033.

<ref name="BIGDSE2015">T. M. Abdellatif, L. F. Capretz, D. Ho. "Software Analytics to Software Practice: A Systematic Literature Review". 1. Int'l Workshop on Big Data Engineering, 2015, pp. 30-36.</ref>

* Raymond P. L. Buse and Thomas Zimmermann. "Information Needs for Software Development Analytics." In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Software Engineering in Practice, Zurich, Switzerland, June 2012, pp. 987-996. [http://research.microsoft.com/apps/pubs/?id=144543 PDF]

<ref name="ASE2011">Dongmei Zhang and Tao Xie. "xSA: eXtreme Software Analytics - Marriage of eXtreme Computing and Software Analytics." In Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), Tutorial, Lawrence, Kansas, November 2011.</ref>

<ref name="IEEESoftware-2013">D. Zhang, S. han, Y. Dan, J.-G. Lou, H Zhang: "Software Analytics in Practice". IEEE Software, Sept./Oct. 2013, pp. 30-35.</ref>

<ref name="CSEETKeynote">Dongmei Zhang. "Software Analytics in Practice and Its Implications for Education and Training." Keynote. In Proceedings of the 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T 2012), Tutorial, Nanjing, China, April 2012.</ref>

*<ref name="CSEETTutorial">Dongmei Zhang, Yingnong Dang, Shi Han, and Tao Xie. "Teaching and Training for Software Analytics." In Proceedings of the 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T 2012), Tutorial, Nanjing, China, April 2012. </ref>

<ref name="MSRKeynote">Dongmei Zhang. "MSR 2012 keynote: Software Analytics in Practice - Approaches and Experiences." In Proceedings of the 9th Working Conference on Mining Software Repositories (MSR 2012), Zurich, Switzerland, June 2012, pp. 1.</ref>

*<ref Raymond P. L.name="ICSETutorial">Dongmei BuseZhang and ThomasTao ZimmermannXie. "InformationSoftware NeedsAnalytics forin SoftwarePractice: DevelopmentMini AnalyticsTutorial." In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Software Engineering in Practice, Mini Tutorial, Zurich, Switzerland, June 2012, pp. 987-996997. [http://research.microsoft.com/appsen-us/pubsgroups/?id=144543sa/softwareanalyticsinpractice_minitutorial_icse2012.pdf PDFSlides]</ref>

*<ref name="Goldfish">Tim Menzies and Thomas Zimmermann. "Goldfish Bowl Panel: Software Development Analytics." In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Software Engineering in Practice, Zurich, Switzerland, June 2012, pp. 1032-1033.</ref>

*<ref name="ICSESEIP12InfoNeeds">Raymond P. L. Buse and Thomas Zimmermann. "AnalyticsInformation Needs for Software Development Analytics." In Proceedings of the Workshop34th onInternational FutureConference ofon Software Engineering Research (FoSERICSE 20102012), SantaSoftware FeEngineering in Practice, NMZurich, USASwitzerland, NovemberJune 20102012, pp. 77987-80996.</ref> [http://research.microsoft.com/apps/pubs/default.aspx?id=136301144543 PDF]

}}

==External links==

* [http://www.infoworld.com/t/applications/turn-application-metrics-business-value-241019/ InfoWorld: Turn application metrics into business value]

* [http://research.microsoft.com/en-us/groups/sa/ Microsoft Research Asia Software Analytics Group (SA)]

* [http://research.microsoft.com/en-us/groups/ese/ Microsoft Research Redmond Empirical Software Engineering Group (ESE)]

* [http://researchconferences.microsoftcomputer.comorg/en-uscseet/groups2012/saCSEET_2012/softwareanalyticsinpractice_approachesandexperiences_msr2012Dongmei.pdf html Software Analytics in Practice –and ApproachesIts Implications for Education and ExperiencesTraining, Keynote slides by Dongmei Zhang at the 9th24th WorkingIEEE-CS Conference on Mining Software RepositoriesEngineering Education and Training (MSRCSEE&T 2012)]

* [http://research.microsoft.com/en-us/groups/sa/softwareanalyticsinpractice_minitutorial_icse2012softwareanalyticsinpractice_approachesandexperiences_msr2012.pdf Software Analytics in Practice – Approaches and Experiences, Mini-tutorialKeynote slides by Dongmei Zhang and Tao Xie at the 34th9th InternationalWorking Conference on Mining Software EngineeringRepositories (ICSEMSR 2012)]

* [http://research.microsoft.com/en-us/groups/sa/softwareanalyticsinpractice_minitutorial_icse2012.pdf Software Analytics in Practice, Mini-tutorial slides by Dongmei Zhang and Tao Xie at the 34th International Conference on Software Engineering (ICSE 2012)]

* [https://sites.google.com/site/xsoftanalytics/ Software Analytics Portal]

* [http://pinterest.com/tomzimmermann/software-analytics/ Software Analytics Pinterest]

* [https://azure.microsoft.com/en-us/services/application-insights/ Microsoft Azure - Application Insights in Azure]

[[Category:Mining Software Repositories]]

[[Category:Software maintenance]]

[[Category:Types of analytics]]

{{software-eng-stub}}