Future of Programming Environments

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CISPA Helmholtz Center for Information Security

The Future of Programming Environments: Integration, Synergy, and Assistance Andreas Zeller, Saarland University Modern programming environments foster the integration of automated, extensible, and reusable tools. New tools can thus leverage the available functionality and collect data from program and process. The synergy of both will allow to automate current empirical approaches. This leads to automated assistance in all development decisions for programmers and managers alike: “For this task, you should collaborate with Joe, because it will likely require risky work on the ‘Mailbox’ class.” http://portal.acm.org/citation.cfm?id=1254727

Technologie

The Future of

Programming Environments
Andreas Zeller
Saarland University

Models Specs Code Traces Proﬁles Tests

e-mail Bugs Effort Navigation Changes Chats

Map bugs to
code locations

Bugs Changes

Eclipse Imports
71% of all components importing compiler
show a post-release defect

import org.eclipse.jdt.internal.compiler.lookup.*;
import org.eclipse.jdt.internal.compiler.*;
import org.eclipse.jdt.internal.compiler.ast.*;
import org.eclipse.jdt.internal.compiler.util.*;
...
import org.eclipse.pde.core.*;
import org.eclipse.jface.wizard.*;
import org.eclipse.ui.*;

14% of all components importing ui
show a post-release defect

Joint work with Adrian Schröter • Tom Zimmermann

Eclipse Imports
Correlation with failure
import org.eclipse.jdt.internal.compiler.lookup.*;
import org.eclipse.jdt.internal.compiler.*;
import org.eclipse.jdt.internal.compiler.ast.*;
import org.eclipse.jdt.internal.compiler.util.*;
...
import org.eclipse.pde.core.*;
import org.eclipse.jface.wizard.*;
import org.eclipse.ui.*;

Correlation with success

Prediction
defect no defect

top 5%

10%
~300 Packages

90%

Software Archives
• contain full record of project history
• maintained via programming environments
• automatic maintenance and access
• freely accessible in open source projects

Bugs Changes

Models Specs Code Traces Proﬁles Tests

“Which modules
should I test most?”

e-mail Bugs Effort Navigation Changes Chats

Models Specs Code Traces Proﬁles Tests

“How long will it take
to ﬁx this bug?”

e-mail Bugs Effort Navigation Changes Chats

Models Specs Code Traces Proﬁles Tests

“This requirement is
risky”

e-mail Bugs Effort Navigation Changes Chats

Studies

Make this
Actionable!

Rosenberg, L. and Hyatt, L. “Developing An Effective Metrics Program”
European Space Agency Software Assurance Symposium, Netherlands, March, 1996

Assistance

Future environments will
• mine patterns from program + process
• apply rules to make predictions
• provide assistance in all development decisions
• adapt advice to project history

Wikis
Joy of Use
Participation Usability
Social Software
Recommendation
Simplicity
Perpetual Beta
Collaboration

Empirical SE 2.0
Trust

Economy
Remixability The Long Tail
DataDriven

Models Specs Code Traces Proﬁles Tests

Program Data

Process Data

e-mail Bugs Effort Navigation Changes Chats

Models Specs Code Traces Proﬁles Tests

Deductive Reasoning

Inductive Reasoning

e-mail Bugs Effort Navigation Changes Chats

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Software-Tests automatisch erzeugen: Frische Ansätze für Forschung, Praxis und Lehre Andreas Zeller, CISPA Helmholtz-Zentrum für Informationssicherheit, Saarbrücken Automatisch erzeugte Softwaretests können mit wenig menschlichem Aufwand viele Fehler finden. In diesem Vortrag stelle ich aktuelle Techniken zur Testerzeugung vor, die für ein gegebenes Programm vollautomatisch dessen Eingabesprache ableiten und aus den so entstehenden Grammatiken große Mengen gültiger Testeingaben ableiten. Unser grammatikbasierter LangFuzz-Testgenerator hat so in den JavaScript-Interpretern von Firefox, Chrome und Edge tausende Fehler gefunden. Die Techniken sind in dem interaktiven Buch “Generating Software Tests” (www.fuzzingbook.org) zusammengefasst. In einer Mischung von Text und Programmcode können Leser im Browser direkt mit den Programmen experimentieren und ihren Code live ergänzen und erweitern. Andreas Zeller ist Forscher am CISPA Helmholtz-Zentrum für Informationssicherheit und Professor für Softwaretechnik an der Universität des Saarlandes, beide in Saarbrücken. Seine Forschung beschäftigt sich mit der Analyse großer Software-Systeme und ihre Entwicklungsgeschichte. In 2010 wurde Zeller zum Fellow der ACM ernannt für seine Beiträge zur automatischen Fehlersuche und der Analyse von Software-Archiven, für die er 2018 als erster Deutscher den Outstanding Research Award der ACM SIGSOFT erhielt. Seine aktuellen Projekte kombinieren Testerzeugung und Specification Mining, gefördert durch den Europäischen Forschungsrat (ERC).

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Andreas Zeller, On Impact in Software Engineering Research Abstract: After 25 years in research, I am happy to have had some impact in Software Engineering Research - some of it on purpose, some of it accidentally. In this talk, I summarize six important lessons I have learned: Work on a real problem, assume as little as possible, keep things simple, have a sound model, keep on learning, and keep on moving. Bio: Andreas Zeller is a full professor for Software Engineering at Saarland University in Saarbrücken, Germany, since 2001. His research concerns the analysis of large software systems and their development process. In 2010, Zeller was inducted as Fellow of the ACM for his contributions to automated debugging and mining software archives, for which he also was awarded 10-year impact awards from ACM SIGSOFT and ICSE. In 2011, he received an ERC Advanced Grant, Europe's highest and most prestigious individual research grant, for work on specification mining and test case generation. In 2013, Zeller co-founded Testfabrik AG, a start-up on automatic testing of Web applications, where he chairs the supervisory board. In 2018, he received the Outstanding Research Award from ACM SiGSOFT.

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Models—abstract and simple descriptions of some artifact—are the backbone of all software engineering activities. While writing models is hard, existing code can serve as a source for abstract descriptions of how software behaves. To infer correct usage, code analysis needs usage examples, though; the more, the better. We have built a lightweight parser that efficiently extracts API usage models from source code—models that can then be used to detect anomalies. Applied on the 200 million lines of code of the Gentoo Linux distribution, we would extract more than 15 million API constraints. On the web site checkmycode.org, anyone can check his/her code against the “wisdom of Linux”.

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Talk recording available! See http://www.st.cs.uni-saarland.de/zeller/Debugging-Debugging/ "Yesterday, my program worked. Today, it does not. Why?" This paper, originally published at ESEC/FSE 1999, introduced the concept of delta debugging, a simple, yet very effective method for automated debugging. In 2009, this paper received the ACM SIGSOFT impact award as the most influential software engineering publication of 1999. In his keynote at ESEC/FSE in Amsterdam on August 27, 2009, Andreas Zeller reviews the state of automated debugging in the past and the future. He shares stories about how delta debugging came to be, how it can be hard to be simple, what programmers really need, and what research should do (and should not do) to explore and cater to these needs. Talk recording available! See http://www.st.cs.uni-saarland.de/zeller/Debugging-Debugging/

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Seeding Bugs to Find Bugs: Mutation Testing Revisited How do you know your test suite is "good enough"? One of the best ways to tell is _mutation testing_. Mutation testing seeds artificial defects (mutations) into a program and checks whether your test suite finds them. If it does not, this means your test suite is not adequate yet. Despite its effectiveness, mutation testing has two issues. First, it requires large computing resources to re-run the test suite again and again. Second, and this is worse, a mutation to the program can keep the program's semantics unchanged -- and thus cannot be detected by any test. Such _equivalent mutants_ act as false positives; they have to be assessed and isolated manually, which is an extremely tedious task. In this talk, I present the JAVALANCHE framework for mutation testing of Java programs, which addresses both the problems of efficiency and equivalent mutants. First, JAVALANCHE is built for efficiency from the ground up, manipulating byte code directly and allowing mutation testing of programs that are several orders of magnitude larger than earlier research subjects. Second, JAVALANCHE addresses the problem of equivalent mutants by assessing the _impact_ of mutations on dynamic invariants: The more invariants impacted by a mutation, the more likely it is to be useful for improving test suites. We have evaluated JAVALANCHE on seven industrial-size programs, confirming its effectiveness. With less than 3% of equivalent mutants, our approach provides a precise and fully automatic measure of the adequacy of a test suite -- making mutation testing, finally, applicable in practice. Joint work with David Schuler and Valentin Dallmeier. Andreas Zeller is computer science professor at Saarland University; he researches large programs and their history, and has developed a number of methods to determine the causes of program failures - on open-source programs as well as in industrial contexts at IBM, Microsoft, SAP and others. His book "Why Programs Fail" has received the Software Development Magazine productivity award in 2006.

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Jeder Programmierer kennt die Situation: Ein Programm läuft nicht so, wie es soll. Ich stelle Techniken vor, die automatisch (a) die Ursachen eines Fehlverhaltens finden - indem wir genau die Aspekte isolieren, die das Zustandekommen eines Fehlers verursachen; (b) Programmfehler finden - indem wir aus dem Code "normale" Anweisungsfolgen lernen und nach Abweichungen suchen; und (c) vorhersagen, wo in Zukunft Fehler auftreten werden - indem wir maschinell lernen, welche Code- und Prozesseigenschaften bisher mit Fehlern korrelierten. Fallstudien an echten Programmen mit echten Fehlern, von AspectJ über Firefox zu Windows demonstrieren die Praxistauglichkeit der vorgestellten Verfahren. Andreas Zeller ist Professor für Softwaretechnik an der Universität des Saarlandes in Saarbrücken. Sein Forschungsgebiet ist die Analyse großer Software-Systeme und deren Fehler. Sein Buch "Why Programs Fail - A Guide to Systematic Debugging" wurde 2006 mit dem Jolt Software Development Productivity Award ausgezeichnet. Vortrag der GI Regionalgruppe Rhein-Neckar 2008-06-19

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Future of Programming Environments

1. The Future of Programming Environments Andreas Zeller Saarland University

2. Programming Environments

3. A Tool Set

4. Tools evolve

5. Tools integrate

6. Tools work together

7. Tools work together

8. Models Specs Code Traces Proﬁles Tests e-mail Bugs Effort Navigation Changes Chats

9. Models Specs Code Traces Proﬁles Tests e-mail Bugs Effort Navigation Changes Chats

10. Bugs Changes

11. Map bugs to code locations Bugs Changes

12. Eclipse Bugs

13. Eclipse Imports 71% of all components importing compiler show a post-release defect import org.eclipse.jdt.internal.compiler.lookup.*; import org.eclipse.jdt.internal.compiler.*; import org.eclipse.jdt.internal.compiler.ast.*; import org.eclipse.jdt.internal.compiler.util.*; ... import org.eclipse.pde.core.*; import org.eclipse.jface.wizard.*; import org.eclipse.ui.*; 14% of all components importing ui show a post-release defect Joint work with Adrian Schröter • Tom Zimmermann

14. Eclipse Imports Correlation with failure import org.eclipse.jdt.internal.compiler.lookup.*; import org.eclipse.jdt.internal.compiler.*; import org.eclipse.jdt.internal.compiler.ast.*; import org.eclipse.jdt.internal.compiler.util.*; ... import org.eclipse.pde.core.*; import org.eclipse.jface.wizard.*; import org.eclipse.ui.*; Correlation with success

15. Prediction defect no defect top 5% 10% ~300 Packages 90%

16. Software Archives • contain full record of project history • maintained via programming environments • automatic maintenance and access • freely accessible in open source projects Bugs Changes

17. Models Specs Code Traces Proﬁles Tests e-mail Bugs Effort Navigation Changes Chats

18. Models Specs Code Traces Proﬁles Tests “Which modules should I test most?” e-mail Bugs Effort Navigation Changes Chats

19. Models Specs Code Traces Proﬁles Tests “How long will it take to ﬁx this bug?” e-mail Bugs Effort Navigation Changes Chats

20. Models Specs Code Traces Proﬁles Tests “This requirement is risky” e-mail Bugs Effort Navigation Changes Chats

21. Models Specs Code Traces Proﬁles Tests e-mail Bugs Effort Navigation Changes Chats

22.

23.

24. Eclipse Bugs

25. Studies Make this Actionable! Rosenberg, L. and Hyatt, L. “Developing An Effective Metrics Program” European Space Agency Software Assurance Symposium, Netherlands, March, 1996

26. “Road map”!

27. Assistance Future environments will • mine patterns from program + process • apply rules to make predictions • provide assistance in all development decisions • adapt advice to project history

28. Wikis Joy of Use Participation Usability Social Software Recommendation Simplicity Perpetual Beta Collaboration Empirical SE 2.0 Trust Economy Remixability The Long Tail DataDriven

29. Challenges

30. Models Specs Code Traces Proﬁles Tests Program Data Process Data e-mail Bugs Effort Navigation Changes Chats

31. Models Specs Code Traces Proﬁles Tests Deductive Reasoning Inductive Reasoning e-mail Bugs Effort Navigation Changes Chats

32. Models Specs Code Traces Proﬁles Tests e-mail Bugs Effort Navigation Changes Chats

33. Models Specs Code Traces Proﬁles Tests e-mail Bugs Effort Navigation Changes Chats

34.

35. Summary

Future of Programming Environments

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