267:
Software
Engineering (ASE 2011), a talk at the International Workshop on Machine Learning Technologies in Software Engineering (MALETS 2011), a tutorial and a keynote talk given by Zhang at the IEEE-CS Conference on Software Engineering Education and Training, a tutorial at the International Conference on Software Engineering - Software Engineering in Practice Track, and a keynote talk given by Zhang at the Working Conference on Mining Software Repositories.
226:
33:
128:"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." --- strengthens the core objectives for methods and techniques of software analytics, focusing on both software artifacts and activities of involved developers and teams.
270:
In
November 2010, Software Development Analytics (Software Analytics with a focus on Software Development) was proposed by Thomas Zimmermann and his colleagues at the Empirical Software Engineering Group (ESE) at Microsoft Research Redmond in their FoSER 2010 paper. A goldfish bowl panel on software
164:
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
266:
research community after a series of tutorials and talks on software analytics were given by Zhang and her colleagues, 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
454:
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,
135:
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
145:
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.
125:"Software analytics aims to obtain insightful and actionable information from software artifacts that help practitioners accomplish tasks related to software development, systems, and users." --- centers on analytics applied to artifacts a software system is composed of.
444:
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
150:
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
360:
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.
490:
Dongmei Zhang and Tao Xie. "Software
Analytics in Practice: Mini Tutorial." In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Software Engineering in Practice, Mini Tutorial, Zurich, Switzerland, June 2012, pp. 997.
470:
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
523:
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.
480:
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
503:
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.
165:
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."
154:
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
513:
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.
179:
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
553:
Software Analytics in Practice and Its Implications for Education and Training, Keynote by Dongmei Zhang at the 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T 2012)
262:
In May 2009, software analytics was first coined and proposed when Dongmei Zhang founded the Software Analytics Group (SA) at Microsoft Research Asia (MSRA). The term has become well known in the
393:
MartĂnez-Fernández, Silverio; Vollmer, Anna Maria; Jedlitschka, Andreas; Franch, Xavier; Lopez, Lidia; Ram, Prabhat; Rodriguez, Pilar; Aaramaa, Sanja; Bagnato, Alessandra (2019).
370:
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.
204:, which support interactively exploring system artifacts and correlated software metrics. There are also software analytics tools using analytical technologies on top of
558:
Software Analytics in Practice – Approaches and Experiences, Keynote slides by Dongmei Zhang at the 9th Working Conference on Mining Software Repositories (MSR 2012)
563:
Software Analytics in Practice, Mini-tutorial slides by Dongmei Zhang and Tao Xie at the 34th International Conference on Software Engineering (ICSE 2012)
239:
it misrepresents the history of software analytics, strengthening a single researcher group that claims to have coined the expression software analytics.
200:
as well as large-scale data computing & processing. For example, software analytics tools allow users to map derived analysis results by means of
557:
172:, "check-ins, work items, bug reports and test executions recorded in software repositories such as CVS, Subversion, GIT, and Bugzilla."
562:
492:
275:
from West Virginia University at the International Conference on Software Engineering, Software Engineering in Practice Track.
17:
537:
348:
D. Zhang, S. han, Y. Dan, J.-G. Lou, H Zhang: "Software Analytics in Practice". IEEE Software, Sept./Oct. 2013, pp. 30-35.
380:
212:
companies, which support assessing software qualities (e.g., reliability), and deriving actions for their improvement.
394:
459:
242:
49:
587:
304:
284:
592:
456:
383:, and Thomas Zimmerman. "Software Development Analytics". Dagstuhl Reports, Vol. 4, Issue 6, pp. 64-83.
209:
552:
197:
572:
395:"Continuously assessing and improving software quality with software analytics tools: a case study"
96:. It aims at describing, monitoring, predicting, and improving the efficiency and effectiveness of
409:
319:
299:
294:
289:
263:
109:
105:
97:
89:
567:
8:
193:
113:
413:
101:
93:
427:
324:
314:
417:
205:
181:
547:
542:
309:
85:
77:
422:
581:
431:
201:
392:
116:, but can also be achieved by collecting user actions or production data.
272:
185:
169:
136:
developers." --- reflects the broad scope including various stakeholders.
81:
189:
548:
Microsoft Research Redmond Empirical Software Engineering Group (ESE)
329:
173:
73:
176:
as well as execution traces or logs can also be taken into account.
132:
46:
it appears to misrepresent the history of software analytics.
538:
InfoWorld: Turn application metrics into business value
271:
development analytics was organized by Zimmermann and
543:
Microsoft Research Asia Software Analytics Group (SA)
356:
354:
351:
112:. The data collection is typically done by mining
579:
342:
131:"Software analytics (SA) represents a branch of
573:Microsoft Azure - Application Insights in Azure
484:
474:
84:, static and dynamic characteristics (e.g.,
464:
497:
235:needs attention from an expert in Software
42:needs attention from an expert in Software
421:
364:
517:
88:) as well as related processes of their
14:
580:
438:
245:may be able to help recruit an expert.
52:may be able to help recruit an expert.
507:
448:
219:
26:
24:
25:
604:
531:
224:
31:
386:
373:
119:
13:
1:
335:
568:Software Analytics Pinterest
305:Software development process
285:Mining Software Repositories
7:
423:10.1109/ACCESS.2019.2917403
278:
237:. The specific problem is:
159:
44:. The specific problem is:
10:
609:
379:Harald Gall, Tim Menzies,
215:
210:agile software development
168:Core data sources include
76:specific to the domain of
198:information visualization
140:
104:, in particular during
114:software repositories
588:Software maintenance
320:Application software
300:Software development
295:Software archaeology
290:Software maintenance
264:software engineering
243:WikiProject Software
110:software maintenance
106:software development
98:software engineering
80:taking into account
50:WikiProject Software
18:Runtime intelligence
414:2019IEEEA...768219M
194:pattern recognition
593:Types of analytics
102:software lifecycle
70:Software analytics
325:Software industry
315:Computer software
260:
259:
67:
66:
16:(Redirected from
600:
525:
521:
515:
511:
505:
501:
495:
488:
482:
478:
472:
468:
462:
452:
446:
442:
436:
435:
425:
399:
390:
384:
377:
371:
368:
362:
358:
349:
346:
255:
252:
246:
228:
227:
220:
206:software quality
182:machine learning
86:software metrics
78:software systems
62:
59:
53:
35:
34:
27:
21:
608:
607:
603:
602:
601:
599:
598:
597:
578:
577:
534:
529:
528:
522:
518:
512:
508:
502:
498:
489:
485:
479:
475:
469:
465:
455:November 2011.
453:
449:
443:
439:
408:: 68219–68239.
397:
391:
387:
381:Laurie Williams
378:
374:
369:
365:
359:
352:
347:
343:
338:
310:User experience
281:
256:
250:
247:
241:
229:
225:
218:
162:
143:
122:
100:throughout the
63:
57:
54:
48:
36:
32:
23:
22:
15:
12:
11:
5:
606:
596:
595:
590:
576:
575:
570:
565:
560:
555:
550:
545:
540:
533:
532:External links
530:
527:
526:
516:
506:
496:
483:
473:
463:
447:
437:
385:
372:
363:
350:
340:
339:
337:
334:
333:
332:
327:
322:
317:
312:
307:
302:
297:
292:
287:
280:
277:
258:
257:
232:
230:
223:
217:
214:
174:Telemetry data
161:
158:
157:
156:
152:
142:
139:
138:
137:
129:
126:
121:
118:
65:
64:
39:
37:
30:
9:
6:
4:
3:
2:
605:
594:
591:
589:
586:
585:
583:
574:
571:
569:
566:
564:
561:
559:
556:
554:
551:
549:
546:
544:
541:
539:
536:
535:
520:
510:
500:
494:
487:
477:
467:
461:
458:
451:
441:
433:
429:
424:
419:
415:
411:
407:
403:
396:
389:
382:
376:
367:
357:
355:
345:
341:
331:
328:
326:
323:
321:
318:
316:
313:
311:
308:
306:
303:
301:
298:
296:
293:
291:
288:
286:
283:
282:
276:
274:
268:
265:
254:
244:
240:
236:
233:This article
231:
222:
221:
213:
211:
207:
203:
202:software maps
199:
195:
191:
187:
183:
177:
175:
171:
166:
153:
149:
148:
147:
134:
130:
127:
124:
123:
117:
115:
111:
107:
103:
99:
95:
91:
87:
83:
79:
75:
71:
61:
58:December 2014
51:
47:
43:
40:This article
38:
29:
28:
19:
519:
509:
499:
486:
476:
466:
450:
440:
405:
401:
388:
375:
366:
344:
269:
261:
248:
238:
234:
178:
167:
163:
155:management).
144:
69:
68:
55:
45:
41:
402:IEEE Access
273:Tim Menzies
251:August 2017
186:data mining
170:source code
151:techniques.
120:Definitions
90:development
82:source code
582:Categories
524:1032-1033.
336:References
208:models in
190:statistics
432:2169-3536
330:Analytics
94:evolution
74:analytics
361:987-996.
279:See also
160:Approach
133:big data
410:Bibcode
216:History
72:is the
514:77-80.
493:Slides
460:Slides
430:
481:2012.
471:2012.
445:2011.
398:(PDF)
428:ISSN
141:Aims
108:and
92:and
457:PDF
418:doi
584::
504:1.
426:.
416:.
404:.
400:.
353:^
196:,
192:,
188:,
184:,
434:.
420::
412::
406:7
253:)
249:(
60:)
56:(
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.