Model-driven Reengineering for a Blue Planet - Refactoring for Energy Efficiency -

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Preston Stafford
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1 Mdel-driven Reengineering fr a Blue Planet - Refactring fr Energy Efficiency - Andreas Winter jint wrk with Marin Gttschalk Jan Jelschen Mirc Jsefik STeSLa

2 "Energy Turnarund" nt far away frm here April 26, 1986 nline: March 1, 1986 Sept. 9, 1988 March 11, STeSLa

3 "Energy Turnarund" 2020: have 35% green pwer 2022: switch ff all nuclear pwer plants April 26, 1986 nline: March 1, 1986 Sept. 9, 1988 March 11, STeSLa

4 "Energy Turnarund" 2020: have 35% green pwer 2022: switch ff all nuclear pwer plants April 26, 1986 nline: March 1, 1986 Sept. 9, STeSLa 2012 March 11,

"Energy Turnarund" Hw will Sftware Engineering Sftware Language Engineering April 26, 1986 Mdel-driven Sftware Reengineering 2020: have 35% green

5 "Energy Turnarund" Hw will Sftware Engineering Sftware Language Engineering April 26, 1986 Mdel-driven Sftware Reengineering 2020: have 35% green pwer 2022: switch ff all nuclear pwer plants help t manage the energy turnver nline: March 1, 1986 Sept. 9, STeSLa 2012 March 11,

Green Sftware Engineering Current Situatin ICT cnsumes mre than 10% f Germanys energy budget (2007) ICT

energy in cmputing centers n perating system level n hardware level des nt cnsider sftware applicatins

infrmatin n user behavir t enable mre energy efficient task scheduling by OS discvering waste f energy early

6 Green Sftware Engineering Current Situatin ICT cnsumes mre than 10% f Germanys energy budget (2007) ICT prduces mre CO 2 emissins than the entire German aviatin sectr Research in Cmputer Science fcusses n saving energy in cmputing centers n perating system level n hardware level des nt cnsider sftware applicatins Applying Sftware-Reengineering analyzing cde fr wasting energy imprving energy inefficient cde prviding infrmatin n user behavir t enable mre energy efficient task scheduling by OS discvering waste f energy early aviding waste f energy by energy aware cmputing cntrlling energy cnsumptin f hardware cmpnents by sftware STeSLa

battery drain mnitr user behavir and deduct usage patterns detect peridic wake-ups and synchrnize Refactring

7 Quality Gal Energy Efficiency Reengineering = Quality Imprvement Static Analysis define and detect energy cde smells define metrics fr energy-efficiency classificatin Dynamic Analysis lg applicatin behavir and crrelate with battery drain mnitr user behavir and deduct usage patterns detect peridic wake-ups and synchrnize Refactring replace energy-inefficient cde intrduce strategy patterns t enable apps t make energy-aware chices STeSLa

Refactring = Identificatin + Restructuring Energy Cde Smells depict energy-inefficient patterns in cde Prcess 1 Preparatin: parse surce t TGraphs 2 Identificatin: identify cde

8 Refactring = Identificatin + Restructuring Energy Cde Smells depict energy-inefficient patterns in cde Prcess 1 Preparatin: parse surce t TGraphs 2 Identificatin: identify cde patterns by graph queries (GReQL) 3 Restructuring: imprve cde thrugh graph transfrmatins 4 Pst prcessing: unparse TGraphs t surce Reengineering Architecture STeSLa

9 Example: GPSPrint Energy Cde Smell binding resurces t early Ptential waste f energy energy inefficient use f GPS sensr [ STeSLa

Resurce Utilizatin in Andrid [cmp. Andrid Develpers: Activities, 2012.

10 Resurce Utilizatin in Andrid [cmp. Andrid Develpers: Activities, STeSLa

11 Example: Binding resurces t early Identificatin Where is the GPS sensr turned n? Where des the lcatinmanager calls requestlcatinupdates() fr the GPS? Bad energy smell call f requestlcatinupdates() in ncreate() STeSLa

12 Metamdel Missin representing Java cde fr efficient querying S Metamdel fine grained Java representatin cntains 86 nde types 67 edge types GPSPrint Graph cntains 9034 ndes edges STeSLa

13 Identificatin finding the smelling call f requestlcatinupdates() direct call indirect call link t enclsing class (directly) STeSLa

14 Restructuring identify energy cde smell identify cde t be refactred d the refactring STeSLa

15 Restructuring Graph Transfrmatin STeSLa

16 Restructuring Graph Transfrmatin using JGraLab STeSLa

17 Further Classes f Energy Cde Smells Lp Bug applicatin is repeating the same activity Dead Cde surce cde which is never used, but laded t memry In-line methd replacing methd calls by the bdy f the called methd Mving t much data unnecessary cmmunicatin between prcessr and memry Immrtality Bug applicatins respawning after explicitly being killed by the user Redundant strage f data different methds f stre the same data in memry Using expensive resurces energy-expensive resurces are exchange t cheaper alternatives (algrithms and hardware cmpnents) Types f Energy Refactrings classical refactring (Fwler) general energy refactrings platfrm specific Types f Energy Analysis static analysis dynamic analysis Remark energy refactrings change behavir f apps STeSLa

Cnclusin Mdel driven Reengineering is applicable t imprve energy-behavir f sftware using TGraphs fr representatin applying GReQL fr querying utilizing JGraLab fr cde restructurings Further

18 Cnclusin Mdel driven Reengineering is applicable t imprve energy-behavir f sftware using TGraphs fr representatin applying GReQL fr querying utilizing JGraLab fr cde restructurings Further investigatin n energy cde smells I like STeSLa finding and defining mre energy cde smell classes and their assciated refactrings prviding an platfrm independent means fr energy quantificatin t prve benefits f energy cde smell refactrings STeSLa

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